Methods and apparatus to correct for deterioration of a demographic model to associate demographic information with media impression information

ABSTRACT

Methods and apparatus to correct for deterioration of a demographic model to associate demographic information with media impression information are disclosed. An example method includes estimating first and second ages of audience members based on demographic information; estimating a third age of an audience member who is not included in the audience members; applying a window function to the second ages to determine a distribution of ages based on the third age; multiplying window values by the first ages to determine corrected first age components; dividing a total of the corrected first age components by a sum of the window values to determine an estimated age of the audience member at a first time; and determining the corrected age of the audience member at a second time based on the estimated age of the audience member at the first time and a time difference between the first and second times.

RELATED APPLICATIONS

This patent claims the benefit of U.S. Provisional Patent ApplicationSer. No. 62/098,787, filed Dec. 31, 2014, which is herein incorporatedby reference in its entirety.

FIELD OF THE DISCLOSURE

This disclosure relates generally to audience measurement, and, moreparticularly, to methods and apparatus to correct for deterioration of ademographic model to associate demographic information with mediaimpression information.

BACKGROUND

Traditionally, audience measurement entities determine audienceengagement levels for media based on registered panel members. That is,an audience measurement entity enrolls people who consent to beingmonitored into a panel. The audience measurement entity then monitorsthose panel members to determine media (e.g., television programs orradio programs, movies, DVDs, advertisements, streaming media, websites,etc.) exposed to those panel members. In this manner, the audiencemeasurement entity can determine exposure metrics for different mediabased on the collected media measurement data.

Techniques for monitoring user access to Internet resources such as webpages, advertisements and/or other Internet-accessible media haveevolved significantly over the years. Some known systems perform suchmonitoring primarily through server logs. In particular, entitiesserving media on the Internet can use known techniques to log the numberof requests received for their media (e.g., content and/oradvertisements) at their server.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system to collect impressions of mediapresented at computing devices and to produce aggregate age-correctiondemographic impression data.

FIG. 2 illustrates an example system to collect impressions of mediapresented at mobile devices and to correct the impressions for agemisattribution at the audience measurement entity.

FIG. 3 illustrates an example implementation of the example agecorrector of FIGS. 1 and 2 to correct age misattributions in mediaimpressions.

FIG. 4 illustrates example age distribution functions used to correctage misattribution.

FIG. 5 illustrates an example data structure to store predicted agedistribution functions for the subscribers of a database proprietor.

FIG. 6 is an example communication flow diagram of an example manner inwhich an audience measurement entity and a database proprietor cancollect impressions and demographic information based on a client devicereporting impressions to the audience measurement entity and thedatabase proprietor.

FIG. 7 is a flow diagram representative of example machine readableinstructions that may be executed to implement the example age correctorof FIGS. 1, 2, and/or 5 to age misattributions in media impressions.

FIG. 8 is a flow diagram representative of example machine readableinstructions that may be executed to implement the example age updaterof FIG. 5 to probabilistically age a prediction age distributionfunction.

FIG. 9 is a flow diagram representative of example machine readableinstructions that may be executed to implement the example demographicimpression aggregator of FIG. 1 to aggregate demographic impressions tobe sent to the audience measurement entity.

FIG. 10 is a block diagram of an example processor system structured toexecute the example machine readable instructions represented by FIG. 7,8, 9 and/or 13 to implement the example age corrector of FIGS. 1, 2, 5,and/or 6.

FIG. 11 depicts an example system to collect impressions of mediapresented on mobile devices and to collect user information fromdistributed database proprietors for associating with the collectedimpressions.

FIG. 12 depicts an example classification tree that may be employed togenerate an age prediction model that uses subscriber activity metricsto predict real ages of subscribers of a database proprietor.

FIG. 13 is a flow diagram representative of example machine readableinstructions that may be executed to implement the example audiencemeasurement entity of FIG. 6 to correct for time-based deterioration ofan age prediction model.

FIG. 14 is a graph of an example distribution that may be generated bythe model corrector of FIG. 3 to correct for deterioration of the ageprediction model for a first example estimated age.

FIG. 15 is a graph of another example distribution that may be generatedby the model corrector of FIG. 3 to correct for deterioration of the ageprediction model for a second example estimated age.

DETAILED DESCRIPTION

Examples disclosed herein may be used to correct age-based demographicgroup misattribution errors in impressions collected by databaseproprietors. As used herein, an impression is an instance of a person'sexposure to media (e.g., content, advertising, etc.). When an impressionis logged to track an audience member's exposure to particular media,the impression may be associated with demographics of the personcorresponding to the impression. This is referred to as attributingdemographic data to an impression, or attributing an impression todemographic data. As used herein, a demographic impression is animpression with attributed demographic data. In this manner, mediaconsumption behaviors of audiences and/or media exposure acrossdifferent demographic groups can be measured. Example demographic groupsinclude demographic groups defined by a range of ages (sometimesreferred to herein as “age-based demographic groups” and/or “demographicbuckets”). For example, demographic buckets may be defined for ages8-12, 13-17, 18-24, 25-34, 35-44, 45-54, 55-64, and 65+. However,age-based demographic group misattribution errors in collectedimpressions can occur when the actual age of the person corresponding tothe impression is different than the reported age of the person. Suchmisattribution errors decrease the accuracy of audience measurement. Toimprove accuracies of impression data having age-based demographic groupmisattribution errors, examples disclosed herein may be used to predictthe actual age of a person, storing that prediction, and calculating anupdated age when an impression corresponding to that person is logged.

In some cases, an age prediction model uses baseline information (e.g.,from a database proprietor) to estimate the age of an audience member(e.g., a subscriber to a service of the database proprietor) when theaudience member is exposed to a media campaign. In some examples, theage prediction model has the highest accuracy that the model can provideon the day the baseline information was obtained, and the accuracy ofthe age prediction model degrades afterwards at some rate ofdeterioration. This accuracy deterioration may be negligible for shortterm measurements. However, using an old model to predict a person's agewould potentially result in biased estimates of the person's age.

Disclosed examples increase the useful life of an age prediction model.That is, disclosed examples lengthen the time that an age predictionmodel can be used to predict ages of anonymous audience members betweencalibrations. Because certain audiences may be anonymous (e.g.,personally identifiable information may not be obtained or obtainable)for technical and/or privacy reasons, measurement of media viewingoccurring over the Internet occurs in aggregate and demographicinformation must be modeled and/or corrected for sources of bias thatare inherent to online audience measurement. Example methods andapparatus disclosed herein provide an improvement to the field ofaudience measurement by increasing the accuracy of measurement foraudience demographics, given a set of audience information that is validat a first time and changes over time. Such audience information may notbe available at a later time. While re-calibration and/or re-training ofa model may be performed, in many cases such re-calibration and/orre-training may be expensive (e.g., to repeatedly purchase new,up-to-date data), and/or impractical or impossible (e.g., if up-to-datedata is not readily available). Disclosed examples increase theefficiency of audience measurement by reducing the computing resourcesthat would be used to repeatedly rebuild an age prediction model toobtain a desired accuracy of the age prediction over time.

An audience measurement entity (AME) measures the composition and sizeof audiences consuming media to produce ratings. Ratings are used byadvertisers and/or marketers to purchase advertising space and/or designadvertising campaigns. Additionally, media producers and/or distributorsuse the ratings to determine how to set prices for advertising spaceand/or to make programming decisions. As increasing numbers of audiencemembers use computers (e.g., desktop computers, laptop computers, etc.),portable devices (e.g., tablets, smartphones, etc.), gaming consoles(e.g., Xbox One®, Playstation® 4, etc.) and/or online media presentationdevices (e.g., Google Chromecast, Roku® Streaming Stick®, etc.)(sometimes collectively referred to herein as “computing devices”) toaccess media, advertisers and/or marketers are interested in accuratelycalculated ratings (e.g., online campaign ratings, etc.) for mediaaccessed on these devices.

To measure audiences on computing devices, an AME may use instructions(e.g., Java, java script, or any other computer language or script)embedded in media as describe below in connection with FIG. 6 to collectinformation indicating when audience members access media on computingdevices. Additionally, one or more user and/or device identifiers (e.g.,an international mobile equipment identity (IMEI), a mobile equipmentidentifier (MEID), a media access control (MAC) address, a web browserunique identifier (e.g., a cookie), an app store identifier, an opensource unique device identifier (OpenUDID), an open deviceidentification number (ODIN), a login identifier, a username, an emailaddress, user agent data, third-party service identifiers, web storagedata, document object model (DOM) storage data, local shared objects, anautomobile vehicle identification number (VIN), etc.) located on acomputing device allow a partnered database proprietor (e.g., Facebook,Twitter, Google, Yahoo!, MSN, Apple, Experian, etc.) to identifydemographic information (e.g., age, gender, geographic location, race,income level, education level, religion, etc.) for the audience memberof the computing device collected via a user registration process. Forexample, an audience member may be accessing an episode of “BoardwalkEmpire” made available by a media streaming service (e.g. via a website,via an app, etc.). In that instance, in response to instructionsexecuting within the browser or the app, a user/device identifier storedon the computing device is sent to the AME and/or a partner databaseproprietor to associate the instance of media exposure (e.g., animpression) to corresponding demographic data of the audience member.The database proprietor can then send logged demographic impression datato the AME for use by the AME in generating, for example, media ratingsand/or other audience measures. In some examples, the partner databaseproprietor does not provide individualized demographic data (e.g.,user-level demographics) in association with logged impressions.Instead, in some examples, the partnered database proprietor providesaggregate demographic impression data (sometime referred to herein as“aggregate census data”). For example, the aggregate demographicimpression data provided by the partner database proprietor may statethat a thousand males, ages 18-24, watched the episode of “BoardwalkEmpire” in the last seven days via computing devices. The aggregatedemographic data from the partner database proprietor does not identifyindividual persons (e.g., is not user-level data) associated withindividual impressions. In some examples, the database proprietorprovides anonymized user-level data (e.g., user-level data withpersonally identifiable information removed) and/or a subset ofanonymized user-level data (e.g., gender, demographic bucket, activitymetrics, race/ethnicity, etc.) to the AME. In this manner, the databaseproprietor protects the privacies of its subscribers/users by notrevealing their identities to the AME.

The AME uses this census data to calculate ratings and/or other audiencemeasures for corresponding media. However, during the process ofregistering with the database proprietor, a subscriber may lie or mayotherwise be inaccurate about the subscriber's age information (e.g.,age, birth date, etc.). For example, a potential subscriber below arequired age (e.g., thirteen, eighteen, twenty-one, etc.) may beprevented from registering with the database proprietor unless thepotential subscriber enters a false, but eligible, birth date. Often insuch examples, the subscriber does not correct the provided age evenafter the user's true age is above the required age. As such,impressions collected by a database proprietor for such subscribers willpotentially be attributed to the wrong age-based demographic group. Forexample, if a ten-year old person, during the registration process,represents that he a thirteen years old, the impression data associatedwith that subscriber will be misattributed to a thirteen-year old personinstead of a ten-year old person.

The effect of large-scale misattribution error may create measurementbias error by incorrectly representing the demographic distribution ofimpressions across a large audience and, therefore, misrepresenting theaudience demographics of impressions collected for advertisements and/orother media to which exposure is monitored by the AME. For example, whensubscribers initially provide inaccurate age information at registration(e.g., report that they are older than they are, etc.), measuredaudience demographics may be skewed older than the actual audiencedemographics. For example, a misattribution error may cause animpression that should be assigned to a certain demographic bucket to beassigned to a different demographic bucket. For example, if the actualage of a subscriber is twenty-two (e.g., in an ages 19-24 demographicbucket), but the reported age of the subscriber is twenty-five (e.g., inan ages 25-34 demographic bucket), the misattribution error would causean impression corresponding to that subscriber to be assigned to thewrong demographic bucket (e.g., the ages 25-34 demographic bucketinstead of the ages 19-24 demographic bucket).

To correct impression data for age-based demographic groupmisattribution errors, the AME uses an age prediction model to predictthe real age of a database proprietor subscriber. The age predictionmodel uses activity metrics (e.g., frequency of login, type of computingdevice used to login, number of connections (e.g., friends, contacts,etc.), privacy settings, etc.) of database proprietor subscribers toassign an age probability density function (PDF) to the subscribers ofthe database proprietor. In some examples, through generating the ageprediction model, sets of age PDFs are defined by the AME. The age PDFsdefine probabilities that the real age of a subscriber is within certaindemographic buckets (e.g., age-range buckets). For example, an age PDFmay state that the probability that the subscriber is in the 2-7year-old demographic bucket is 0%, the probability that the subscriberis in the 8-12 year-old demographic bucket is 0%, the probability thatthe subscriber is in the 13-17 year-old demographic bucket is 7%, theprobability that the subscriber is in the 18-24 year-old demographicbucket is 18%, the probability that the subscriber is in the 25-34year-old demographic bucket is 63%, the probability that the subscriberis in the 35-44 year-old demographic bucket is 11%, the probability thatthe subscriber is in the 45-54 year-old demographic bucket is 1%, theprobability that the subscriber is in the 55-64 year-old demographicbucket is 0%, and the probability that the subscriber is in the 65+year-old demographic bucket is 0%.

The predicted age of the subscriber predicted by the age predictionmodel is used as part of the demographic data that is attributed to theimpression. However, the predictive value of the age prediction modeldegrades over time as subscriber behaviors (e.g., as measured bysubscriber activity metrics) on which the age prediction model is basedchange over time. For example, an age prediction model can be developedon day d=0, based on subscriber behaviors that are true as of day d=0for different age groups. As subscriber behaviors change for aparticular age group over time, the same age prediction model willgenerate inaccurate results when used at a later time. For example, atday d=0, a typical 21-24 year-old male may log into Internet servicesvia a mobile phone for 30% of his total logins. However, at day d=183 (6months), 21-24 year old males might log in to Internet services usingmobile phones for 50% of total logins. As such, the age prediction modelgenerated at day d=0 based on a device type criterion will generateinaccurate results when used on day d=183 due to how log in behaviorshave generally changed for subscribers in the 21-24 year-old maleage-based demographic group. To counteract the degradation of the ageprediction model's predictive value, the AME may, from time to time,regenerate the model. However, generating the age prediction modelrequires considerable resources (e.g., time, processing power,bandwidth, memory usage, etc.). Between the time the age predictionmodel is generated and the time that the age prediction model is used topredict age, the likelihood that some age predictions (e.g., theterminal node assignments) made by the age prediction model may beinaccurate increases over time.

As disclosed below, to increase accuracies of ages attributed toimpressions between the time the age prediction model is generated andthe time that the age prediction will be regenerated, the AME and/or thedatabase proprietor predicts the ages of the subscribers of a databasesubscriber when the age prediction model is initially generated. The agepredictions are stored in an age-cache with, for example, a useridentifier (UID), an age PDF assigned by the age prediction model, andthe date the prediction was made. A UID is used by the databaseproprietor to identify user activity. For example, a UID may be adevice/user identifier, a user name, an alphanumeric code randomlygenerated when the user registers, an anonymized identifier, an emailaddress, etc. In some examples, the AME and/or the database proprietor,to protect subscriber privacy, do not store activity metrics on whichthe age prediction is based in the age-cache.

When impression data corresponding to a subscriber is received, insteadof generating a new age PDF through the age prediction model, thepredicted age PDF for that subscriber is retrieved from the age-cacheand is probabilistically aged. That is, the age PDF is adjusted toaccount for the probability of the subscriber aging into a newdemographic bucket between the date that the age prediction model wasused and the date the impression was logged. The adjusted age PDF isthen attributed to the corresponding impression.

The AME may, from time to time, still regenerate the age predictionmodel. However, the time between regenerating the age prediction modelmay be extended because the degradation of the model has beenameliorated by probabilistically aging the predicted age PDFs. In such amanner, computing resources are conserved by decreasing the number oftimes the age prediction model needs to be generated to determineacceptably accurate ages. In some examples, upon regenerating the ageprediction model, the AME and/or database proprietor may use the ageprediction model to assign predicted age PDFs to the subscribers of thedatabase proprietor. In some examples, the AME and/or databaseproprietor may assign age PDFs to all of the subscribers aftergenerating an age prediction model. In some examples, to conserveprocessing resources, the AME and/or database proprietor may use the ageprediction model to assign predicted age PDFs to subscribers that haveregistered with the database proprietor since the last age predictionmodel was generated (e.g., new subscribers).

Disclosed example methods correct for deterioration of a demographicmodel to associate demographic information with media impressioninformation. Some example methods include collecting, at a processor atan audience measurement entity, messages indicating first impressions ofa media item delivered to devices via the Internet. In some examples,the messages identify the media item presented at the devices. Someexample methods include receiving, at the processor at the audiencemeasurement entity, first demographic information describing firstnumbers of impressions of the media item and first numbers of audiencemembers attributed to respective demographic groups by a databaseproprietor. In some example methods, the first numbers of theimpressions and the first numbers of audience members correspond to thefirst impressions of the media. Some disclosed example methods includeestimating first ages of the audience members based on the firstdemographic information, estimating second ages of the audience membersbased on the first demographic information, and estimating a third ageof an audience member who is not included in the audience members fromthe database proprietor. In some example methods, the first agescorrespond to a first time, the second ages and the third age correspondto a second time after the first time. Disclosed example methods includeestimating a corrected age of the audience member at the second time. Insome example methods, the estimating the corrected age includes applyinga window function to the second ages to determine a distribution of agesbased on the third age of the audience member as a mean of thedistribution, multiplying window values of the second ages by respectiveones of the first ages to determine corrected first age components,summing the corrected first age components and dividing a total of thecorrected first age components by a sum of the window values todetermine an estimated age of the audience member at the first time, anddetermining the corrected age of the audience member at the second timebased on the estimated age of the audience member at the first time anda time difference between the first and second times. Disclosed examplemethods also include determining ratings information for the media byattributing impressions and audience counts to the media using thecorrected age of the audience member instead of the third age.

In some example methods, the window function includes a probabilitydensity function based on a Gaussian distribution. In some examples, theestimating of the first ages includes determining a predicted ageprobability density function. In some examples, the estimating of thesecond ages includes applying an aging factor to an age bucket in thepredicted age probability density function. Some disclose examplemethods further include selecting the audience members from a larger setof audience members based on the second ages being within an age range.In some examples, the age range is based on the third age.

Some disclosed example methods further include transmitting audiencemeasurement entity identifiers to the devices in response to at least aportion of the messages. In some examples, the estimating of the thirdage is in response to determining, based on the audience member notbeing associated with an audience measurement entity identifier, thatthe audience member has not been previously identified. Some disclosedexample methods further include sending re-direct messages in responseto at least a portion of the messages. In some examples, the re-directmessages cause at least a portion of the devices to send third messagesto the database proprietor and the first demographic information isreceived based on the third messages.

Disclosed example apparatus to associate demographic information withmedia impressions and audience using a deteriorated demographic modelinclude a first impressions collector, a second impressions collector,an age predictor, a model corrector, and a ratings determiner. In somedisclosed examples, the first impressions collector collects messagesindicating first impressions of a media item delivered to devices viathe Internet. In some examples, the messages identify the media itempresented at the devices. In some disclosed example apparatus, thesecond impressions collector receives first demographic informationdescribing first numbers of impressions of the media item and firstnumbers of audience members attributed to respective demographic groupsby a database proprietor. In some examples, the first numbers of theimpressions and the first numbers of audience members corresponding tothe first impressions of the media. In some disclosed example apparatus,the age predictor to estimates first ages of the audience members basedon the first demographic information, estimates second ages of theaudience members based on the first demographic information, andestimates a third age of an audience member who is not included in theaudience members from the database proprietor. In some examples, thefirst ages correspond to a first time and the second ages and the thirdage correspond to a second time after the first time. In some disclosedexamples, the model corrector applies a window function to the secondages to determine a distribution of ages based on the third age of theaudience member as a mean of the distribution, multiplies window valuesof the second ages by respective ones of the first ages to determinecorrected first age components, divides a sum of the corrected first agecomponents by a sum of the window values to determine an estimated ageof the audience member at the first time, and determines the correctedage of the audience member at the second time based on the estimated ageof the audience member at the first time and a time difference betweenthe first and second times. In some disclosed apparatus, the ratingsdeterminer determines ratings information for the media by attributingimpressions and audience counts to the media using the corrected age ofthe audience member instead of the third age.

In some examples, the model corrector applies the window function byapplying a probability density function based on a Gaussiandistribution. In some example apparatus, the age predictor is toestimate the first ages by determining a predicted age probabilitydensity function. In some examples, the age predictor is to estimate thesecond ages by applying an aging factor to an age bucket in thepredicted age probability density function.

In some disclosed example apparatus, the model corrector selects theaudience members from a larger set of audience members based on thesecond ages being within an age range, the age range being based on thethird age. In some disclosed examples, the first impressions collectortransmits audience measurement entity identifiers to the devices inresponse to at least a portion of the messages. In some examples, theage predictor estimates of the third age in response to determining,based on the audience member not being associated with an audiencemeasurement entity identifier, that the audience member has not beenpreviously identified.

In some disclosed example apparatus, the first impressions collectorsends re-direct messages in response to at least a portion of themessages, the re-direct messages to cause at least a portion of thedevices to send third messages to the database proprietor, the firstdemographic information being received based on the third messages.

FIG. 1 illustrates an example system 100 to collect impression data 102of media presented at computing devices 104 (e.g., desktop computers,laptop computers, tablets, smartphones, gaming consoles, GoogleChromecast, Roku® Streaming Stick®, etc.) and to produce aggregateage-corrected demographic impression data 106 to be used by an audiencemeasurement entity (AME) 108. In the illustrated example, the impressiondata 102 is collected by a database proprietor 110 (e.g., Facebook,Twitter, Google, Yahoo!, MSN, Apple, Experian, etc.). In some examples,the impression data 102 and/or separate impression data is alsocollected by the AME 108. In some examples, the aggregate demographicimpressions 106 are used by the AME 108 to generate ratings for mediaaccessed at computing devices (e.g., the computing device 104.)

In the illustrated example, after media is accessed on the computingdevice 104, the computing device 104 reports the impression data 102 tothe database proprietor 110. In some examples, the computing device 104reports impression data 102 for accessed media based on instructionsembedded in the media that instruct the computing device 104 (e.g.,instruct a web browser or an app in the computing device 104) to sendimpression data 102 to the database proprietor 110. The exampleimpression data 102 includes an impression request 112 and a device/useridentifier 114. The example impression request 112 includes a mediaidentifier (e.g., an identifier that can be used to identify content, anadvertisement, and/or any other media) corresponding to the mediaaccessed on the computing device 104. In some examples, the AME 108modifies and/or encodes the media identifiers so the database proprietor110 cannot identify the media. In some examples, the impression request112 also includes a site identifier (e.g., a URL) of a website (e.g.,YouTube.com, ABC.com, Hulu.com, etc.) that served the media to thecomputing device 104 and/or a host website ID (e.g., sbnation.com) ofthe website that displays or presents the media. In some examples, theimpression request 112 includes an impression identifier that may beused to uniquely identify the impression request. In some examples, thedevice/user identifier 114 is a device identifier (e.g., aninternational mobile equipment identity (IMEI), a mobile equipmentidentifier (MEID), a media access control (MAC) address, etc.), a webbrowser unique identifier (e.g., a cookie), a user identifier (e.g., auser name, a login ID, etc.), an Adobe Flash® client identifier,identification information stored in an HTML5 datastore, a vehicleidentification number (VIN) and/or any other identifier that thedatabase proprietor 110 stores in association with demographicinformation about subscribers corresponding to the computing devices104. In some examples, the database proprietor 110 uses the device/useridentifier 114 to determine if the user of the computing device 104 is asubscriber of the database proprietor 110.

In the illustrated example of FIG. 1, the database proprietor 110includes an example impression records database 116, an exampleage-corrected demographic impression records database 118, an exampleimpression handler 120, an example subscriber accounts database 122, anexample age corrector 124, and an example demographic impressionaggregator 126. The example impression records database 116 stores theimpression data 102 received from the computing device 104. The exampleage-corrected demographic impression records database 118 storesage-corrected impression data generated by the example impressionhandler 120. In some examples, the impression records database 116 andthe example age-corrected impression records database 118 may be thesame database.

In the illustrated example, the impression handler 120 generatesdemographic impression data by matching demographic information from thesubscriber accounts database 122 to the impression data 102. In theillustrated example, the impression handler 120 uses the user/deviceidentifier 114 to determine if the impression data 102 is associatedwith a subscriber corresponding to an account in the subscriber accountsdatabase 122. In some examples, a value (e.g., a user/device identifier)matching the user/device identifier 114 is stored in a correspondingsubscriber account record in the subscriber accounts database 122. Insome examples, the impression handler 120 may store and/or otherwiseaccess an intermediate table that associates the user/device identifiers114 with identifiers used to index subscribers (e.g., a subscriberidentifier 130) in the subscriber accounts database 122. For example,the impression handler 120 may look up on an intermediate table a cookievalue of a cookie used as a user/device identifier 114 to determine thesubscriber identifier 130. In some examples, if the database proprietor110 cannot find a match for the user/device identifier 114 and auser/device identifier in the subscriber accounts database 122, thecorresponding impression data 102 is not processed further. The exampleimpression handler 120 retrieves demographic information correspondingto the subscriber identifier 130 from the subscriber accounts database122.

In the illustrated example, the example impression handler 120 sends anage-correction request 128 to the example age corrector 124. In theillustrated example, the age-correction request 128 includes a useridentifier 130 (UID) (e.g., the user/device identifier 114, thesubscriber identifier, etc.) and an impression date 132. The exampleimpression date 132 is a date corresponding to when the impression data102 was generated by the computing device 104 (e.g. included with theimpression data 102 when the impression data is sent to the databaseproprietor 110). Based on the UID 130 and the impression date 132, theexample age corrector 124 generates an age-corrected probability densityfunction (PDF) 134. In some examples, the age-corrected PDF 134 defineprobabilities that the real age of the subscriber corresponding to thesubscriber identifier 130 is within certain demographic buckets (e.g.,demographic groups defined by age ranges). For example, theage-corrected PDF 134 may indicate that the probability of thesubscriber being in the 18-21 age range is 11.6%, the probability of thesubscriber being in the 22-27 age range is 44.5%, the probability of thesubscriber being in the 28-33 age range is 36.7%, and the probability ofthe subscriber being in the 34-40 age range is 7.2%.

In some examples, the age corrector 124 generates and/or uses an ageprediction model to estimate ages (or age ranges) of subscribers. Theexample age prediction model is trained and/or calibrated fromtime-to-time, but is not constantly calibrated for accuracy. As aresult, over time the age prediction model used by the age corrector 124becomes outdated and the accuracy of the age prediction model isreduced. The example age corrector 124 performs corrections fortime-based deterioration of age prediction model(s) used to predict agesof subscribers.

In the illustrated example, the impression handler 120 generates ademographic impression by attributing the demographic data (e.g.,without the subscriber-reported age) retrieved from the subscriberaccounts database 122 and the age-corrected PDF 134 to the impressiondata 102. The example impression handler 120 stores the demographicimpressions in the example age-corrected demographic impression recordsdatabase 118.

The example demographic impression aggregator 126 creates aggregatedemographic impressions 106 using the demographic impressions stored inthe example age-corrected demographic impression database 118. Byproviding aggregate demographic impressions 106, the database proprietor110 protects the privacies of its subscribers by not revealing theiridentities or subscriber-level media access activities, to the AME 108.In some examples, the demographic impressions are aggregated by media.For example, the demographic impression aggregator 126 may createaggregate demographic impressions 106 for season two, episode one of thetelevision program titled “Boardwalk Empire.” In some examples, thedemographic impressions are aggregated for a certain time period. Forexample, the demographic impression aggregator 126 may create aggregatedemographic impressions 106 for an advertisement for a food product,such as a “Kellogg's® Eggo® Cinnamon Toast Waffles” commercial, for thepast seven days. The example demographic impression aggregator 126creates aggregate demographic impressions 106 by demographic group(e.g., gender, income level, race/ethnicity, marital status, etc.). Forexample, the demographic impression aggregator 126 may create aggregatedemographic impressions 106 for unmarried males accessing the televisionprogram titled “Being Human.”

In some examples, the demographic impressions are aggregated asgranularly (e.g., aggregated by many demographic categories (e.g.,ethnicity, religion, education level, gender, income, etc.)) or ascoarsely (e.g., aggregated by only a few demographic categories) asagreed between the database proprietor 110 and the AME 108. For example,the database proprietor 110 and the AME 108 may agree that the databaseproprietor 110 will provide aggregate demographic impressions 106 forthe television program titled “Scandal” categorized by gender,ethnicity, and education level.

To generate the aggregate demographic impressions 106, the demographicimpression aggregator 126 aggregates the age-corrected PDF 134 stored inthe age-corrected demographic impression records database 118. In someexamples, to aggregate the age-corrected PDFs, the demographicimpression aggregator 126 sums the age probability value for acorresponding demographic bucket of the demographic impressions beingaggregated and divides the summed probability values by the number ofdemographic impressions being aggregated. In some such examples, thedemographic impression aggregator 126 repeats this process for eachdemographic bucket. In some examples, the demographic impressionaggregator 126 generates an aggregate age-corrected PDF in accordancewith Equation 1 below.

$\begin{matrix}{{AAP}_{j} = \frac{\sum_{i = 0}^{n}{{ADF}_{j}(i)}}{n}} & {{Equation}\mspace{14mu} 1}\end{matrix}$

In Equation 1 above, AAP_(j) is the aggregated age probability fordemographic bucket j, i is a demographic impression, n is the number ofdemographic impressions to be included in the aggregate demographicimpressions 106, and ADF_(j) is the age-corrected PDF probability valuefor demographic bucket j. In some examples, the demographic impressionaggregator 126 compiles the aggregated age probabilities (AAP_(j)) forthe demographic buckets into the aggregate age-corrected PDF that isincluded in the aggregate demographic impression 106.

Consider the following example for males that accessed season 1, episode2 of a television show titled “Being Human” in the last seven days. Theexample corrected-age distribution functions 134 for demographicimpressions A through D are given in table 1 below.

TABLE 1 EXAMPLE AGE-CORRECTED PROBABILITY DENSITY FUNCTIONSCORRESPONDING TO EXAMPLE DEMOGRAPHIC IMPRESSIONS A THROUGH D Age 12-14Age 15-17 Age 18-20 Age 21-24 Age 25-29 Age 30-34 A 0.200 0.350 0.3000.100 0.025 0.025 B 0.000 0.700 0.150 0.100 0.000 0.050 C 0.000 0.0001.000 0.000 0.000 0.000 D 0.100 0.350 0.400 0.075 0.075 0.000In the illustrated example, the aggregate age probability for the agerange 12-14 demographic bucket is 7.5% ((0.2+0.0+0.0+0.1)/4), theaggregate age probability for the age range 15-17 demographic age bucketis 35% ((0.35+0.7+0.0+0.35)/4), the aggregate age probability for theage range 18-20 demographic age bucket is 46.3% ((0.3+0.15+1.0+0.4)/4),the aggregate age probability for the age range 21-24 demographic agebucket is 6.9% (0.1+0.1+0.0+0.075)/4), the aggregate age probability forthe age range 25-29 demographic age bucket is 2.5%(0.025+0.0+0.0+0.075)/4), and the aggregate age probability for the agerange 30-34 demographic age bucket is 1.8% ((0.025+0.05+0.0+0.0)/4).Based on the example corrected-age distribution functions in Table 2above, the aggregate corrected-age PDF attributed to the aggregatedemographic impressions 106 corresponding to males that accessed season1, episode 2 of the television program titled “Being Human” in the lastseven days is given in Table 2 below.

TABLE 2 EXAMPLE AGGREGATE AGE-CORRECTED PROBABILITY DENSITY FUNCTION Age12-14 Age 15-17 Age 18-20 Age 21-24 Age 25-29 Age 30-34 0.075 0.3500.463 0.069 0.025 0.018

In the illustrated example, from time to time, the age corrector 124retrieves subscriber data 136 from the subscriber accounts database 122in order to predict age PDFs for the subscribers of the databaseproprietor 110. In some examples, the age corrector 124 retrievessubscriber data 136 after a new prediction model is generated. Theexample subscriber data 136 includes the example UID 130 and examplesubscriber activity metrics 138. The example subscriber activity metrics138 are collected and/or calculated based on subscriber behavior and/ordemographic factors. The example subscriber activity metrics 138 includeself-reported demographic data and behavioral data of a subscriber ofthe database proprietor 110. The example subscriber activity metrics 138include login frequency, mobile login activity, privacy settings, postrate, number of contacts, days since registration, whether a cell phonenumber has been included in registration information, etc. Using theexample UID 130 and example subscriber activity metrics 138, the exampleage corrector 124 predicts and stores age PDFs for the subscriberscorresponding to the retrieved subscriber data 136.

FIG. 2 illustrates an example system 200 to collect impression data 102of media accessed at computing devices and to correct the impressiondata 102 for age misattribution at the AME 108. In the illustratedexample, the computing device 104 sends impression data 102 to the AME108. In some examples, the computing device 104 sends impression data102 to the database proprietor 110. The example database proprietor 110sends anonymized subscriber demographic data 202 to the AME 108. Theexample anonymized subscriber demographic data 202 is demographic datafrom a subscriber database (e.g., the subscriber database 122 of FIG. 1)that has one or more items of personally identifiable information (e.g.,name, home address, email address, date of birth, birthplace, telephonenumber, national identification number, etc.) removed. In some examples,the anonymized subscriber demographic data 202 also includes anidentifier that may be used to match the anonymized subscriberdemographic data 202 with impression data 102. In some examples, thedatabase proprietor 110 provides the anonymized subscriber demographicdata 202 in response to receiving the impression data 102. In someexamples, the database proprietor 110 provides the anonymized subscriberdemographic data 202 in response to receiving a request from the AME 108for the anonymized subscriber demographic data 202. In some examples,the database proprietor 110 provides, from time to time (e.g., atperiodic or aperiodic intervals such as every 24 hours or when aparticular amount of data has been collected), the anonymized subscriberdemographic data 202 in bulk.

In some examples, the database proprietor 110 provides anonymizedidentifiers with the anonymized subscriber demographic data 202. In someexamples, to facilitate correlating the impression data 102 with theanonymized subscriber demographic data 202, the database proprietor 110sends subscriber correlation data 205 to the AME 108. In some examples,the subscriber correlation data 205 includes the impression identifierincluded in the impression request 112 and the corresponding anonymizedidentifier. In some examples, the subscriber correlation data 205 issent to the AME 108 in response to the impression data 102 beingreceived by the database proprietor 110. In some examples, the databaseproprietor 110 sends subscriber correlation data 205 to the AME 108 fromtime to time (e.g., at periodic or aperiodic intervals such as every 24hours or when a particular amount of impression data 102 has beencollected). In such a manner, the anonymized identifier allows the AME108 to correlate anonymized subscriber demographic data 202 withimpression data 102 without allowing the AME 108 to identify theparticular subscriber that caused the impression data 102 to begenerated.

In the illustrated example, the AME 108 includes an example anonymizedsubscriber database 203, an example impressions collector 204, anexample age corrector 124, an example age adjuster 206, and an exampleage-adjusted impressions database 208. The example anonymized subscriberdatabase 203 stores the example anonymized subscriber demographic data202 received from the example database proprietor 110. In the exampleillustrated in FIG. 2, the impressions collector 204 receives theimpression data 102 from the computing device 104. The exampleimpressions collector 204 generates demographic impressions byattributing anonymized subscriber data 202 with impression data 102. Insome examples, the impressions collector 204 compares an impressionidentifier (e.g., the impression identifier 1140 of FIG. 11 below) ofthe impression request 112 included in the impression data 102 tosubscriber correlation data 205 received from the database proprietor110. In such examples, if a corresponding impression identifier isidentified, the impression data 102 is attributed to the anonymizedsubscriber data 202 corresponding to the anonymized identifier includedwith the subscriber correlation data 205.

In the illustrated example, the AME 108 receives subscriber data 136 forone or more subscribers of the database proprietor 110. The examplesubscriber data 136 includes a user identifier 130 and subscriberactivity metrics 138. In some examples, the user identifier 130 is theanonymized identifier included in the anonymized subscriber demographicdata 202. In some examples, the subscriber data 136 has personallyidentifiable information removed. Using the user identifier 130 and theexample subscriber activity metrics 138, the example age corrector 124predicts and stores age PDFs for the subscribers corresponding to theanonymized subscriber data 205. For example, the example age adjuster206 sends an age-correction request 128 to the example age corrector124. In the illustrated example, the age-correction request 128 includesa user identifier 130 (e.g., an anonymized subscriber identifier, auser/device identifier 114, etc.) and an impression date 132. Based onthe user identifier 130 and the impression date 132, the example agecorrector 124 generates an age-corrected PDF 134. The example ageadjuster 206 attributes the age-corrected PDF 134 to the demographicimpression data and stores the demographic impression data in theage-adjusted impression database 208.

FIG. 3 illustrates an example implementation of the example agecorrector 124 of FIGS. 1 and 2 to correct age misattributions indemographic impression data. The example age corrector 124 generatescorrected age PDF(s) 134 of subscribers registered with a databaseproprietor (e.g., the database proprietor 110 of FIGS. 1 and 2) based onsubscriber activity metrics (e.g., frequency of login, type of computingdevice used to login, number of connections (e.g., friends, contacts,etc.)), privacy settings, etc.). In the illustrated example, when theage corrector 124 receives an age request 128, the age corrector 124generates the corrected age PDF(s) 134. In some examples, the agerequests 128 include one or more user identifiers (UIDs) 130 (e.g., auser/device identifier 114, an anonymized identifier, etc.) withcorresponding impression dates 302 on which one or more impressions werelogged (e.g., by the database proprietor 110) in association with theUID(s) 130. The example corrected age PDF(s) 134 define probabilitiesthat the real age of a subscriber is within certain demographic bucketsas of a corresponding impression date 302.

In the illustrated example of FIG. 3, the age corrector 124 includes anexample age predictor 304, an example age prediction model 306, anexample age cache 308, and an example age updater 310. The example agepredictor 304 is structured to predict an age PDF 305 for a subscriberof the database proprietor 110 based on subscriber data 136 (FIG. 1)provided by the database proprietor 110. The age PDFs 305 defineprobabilities that the real age of the subscriber is within certaindemographic buckets on the date the prediction is made (e.g., the dateon which the age PDF 305 is determined). The example age predictor 304stores the predicted age PDF(s) in the age cache 308 with the UID 130and the date that the age PDF was predicted. In some examples, toprotect subscriber privacy, the age predictor 304 does not store thesubscriber activity metrics 138 provided with the subscriber data 136 inthe age cache 308.

The example age prediction model 306 receives the subscriber activitymetrics 138 included with the subscriber data 136 and generates agePDFs. The AME 108 generates the example age prediction model 306 byanalyzing subscriber activity metrics of subscribers of the databaseproprietor 110 that are also enrolled as panelists by the AME 108(FIG. 1) and the demographic information supplied by the panelist (e.g.,when the panelist is enrolled). For example, when a person is enrolledto be a panelist, the person informs the AME 108 of which databaseproprietors 110 the person subscribes. In that scenario, the AME 108obtains consent from the enrolled panelists to use the subscriberactivity metrics 138 from the database proprietor 110 and thedemographic information collected by the AME 108 to contribute to an ageprediction model 306. While the self-reported demographic information(e.g., age, etc.) reported to the database proprietor 110 is generallyconsidered inaccurate, the demographic information collected from thepanelist (e.g., via a survey, etc.) by the AME 108 is considered highlyaccurate.

In some examples, the age prediction model 306 is implemented using aclassification tree. In some such examples, using the subscriberactivity metrics 138 and demographic data of the panelists, aclassification tree model is generated comprising intermediate (e.g.,decision) nodes and terminal (e.g., prediction) nodes. The intermediatenodes contain test conditions based on the subscriber activity metrics138 and demographic data and result in branching paths. For example, anintermediate node may branch in one direction if a login frequency(e.g., the number of times a week the subscriber logs into the databaseproprietor 110) is greater than five times per week, and may branch inanother direction if the login frequency is less than or equal to fivetimes per week. The branching paths may either lead to anotherintermediate node or a terminal node.

In such a scenario, the terminal nodes represent a classification (e.g.,a predicted age PDF 305) based on the subscriber activity metrics 138and demographic data of the panelist. After the classification tree isconstructed, the panelists' data is application to the classificationtree so that the panelists are classified into the terminal nodes. Insome such examples, the age PDF 305 associated with that terminal nodeis percentage of panelists in the terminal node that fall within acorresponding demographic bucket based on the panelists' real ages. Forexample, of the panelists in a terminal node, 0% of the panelist may bewithin the 2-7 year-old demographic bucket, 0% of the panelist may bewithin the 8-12 year-old demographic bucket, 7% of the panelist may bewithin the 13-17 year-old demographic bucket, 18% of the panelist may bewithin the 18-24 year-old demographic bucket, 63% of the panelist may bewithin the 25-34 year-old demographic bucket, 11% of the panelist may bewithin the 35-44 year-old demographic bucket, 1% of the panelist may bewithin the 45-54 year-old demographic bucket, 0% of the panelist may bewithin the 55-64 year-old demographic bucket, and 0% of the panelist maybe within the 65+ year-old demographic bucket. Examples that may be usedto generate the age prediction model 306 are disclosed in U.S. patentapplication Ser. No. 13/209,292, entitled “Methods and Apparatus toAnalyze and Adjust Demographic Information,” which is incorporated byreference in its entirety.

In some examples, when the age prediction model 306 is generated, atable or a database is created that correlates an age PDF identifier(ID) with an age PDF 305. In some such examples, the age predictionmodel 306 outputs the age PDF identifier (ID) corresponding to thepredicted age PDF 305. In some such examples, the age predictor 304looks up the age PDF ID in a table and/or database to retrieve thepredicted age PDF 305 to store in the age cache 308. In some examples,the age predictor 304 stores the age PDF ID in the age cache 212.Alternatively, in some examples, the age prediction model 306 outputsthe predicted age PDF 305, which is stored by the age predictor 304 inthe age cache 308.

The example age corrector 124 of FIG. 3 further includes a modelcorrector 312. As the age prediction model 306 ages between training orcalibration sessions, the age prediction model 306 is subject todeterioration (e.g., loss of accuracy). Due to the costs and/or timeinvolved in training an age prediction model, the model may be reused topredict panelist or audience member ages for as long as the projectedaccuracy of the model is within acceptable limits. Because the ageprediction model does not require correction for deterioration at thetime the model is trained, the example model corrector 312 performsmodel correction for audience members who are newly added to thedemographic data at a later time.

To correct for model deterioration, the example model corrector 312 ofFIG. 3 receives estimated first ages 314 of a group of audience membersand estimated second ages 316 of the audience members (e.g., based ondemographic information received from a database proprietor). Theexample age predictor 304 estimates the first ages and the second agesusing the age prediction model 306. The first ages are associated withthe time at which the age prediction model was trained, and the secondages correspond to a later time at which the age prediction model isbeing used. At the later time, the age predictor 304 estimates a thirdage of a new audience member or panelist who is not included in thegroup of audience members. The third age corresponds to the later time,rather than to the first time.

The example model corrector 312 estimates the age of the new audiencemember at the first time and then determines the age of the new audiencemember at the second time using the estimated age at the first time. Themodel corrector 312 applies a window function (e.g., a Gaussiandistribution, also called a normal distribution) to the second ages todetermine a distribution of the second ages using the third age (of thenew audience member) as a mean of the distribution. As a result, each ofthe example second ages corresponds to a window value (e.g., aprobability density function value) based on the distribution. Theexample model corrector 312 multiplies the window values of the secondages by respective ones of the first ages to determine corrected firstage components. The example model corrector 312 sums the corrected firstage components to determine an estimated age of the audience member atthe first time.

The example model corrector 312 determines the corrected age of theaudience member at the later time based on the estimated age of theaudience member at the first time and a time difference between thefirst and later times. For example, if the later time is 2 years afterthe first time, the example model corrector 312 adds 2 years to theestimated age of the audience member at the first time. The examplemodel corrector 312 provides a corrected age 318 to the example agepredictor 304 to enhance the accuracy of demographics attributed toimpressions and/or unique audience counts for media.

FIG. 4 shows an example terminal node table 400 showing age PDFs 305a-305 c. In the illustrated example, the age PDFs 305 a-305 c on theterminal node table 400 are identified by age PDF IDs 404 (APDFID 404).The example age PDFs 305 a-305 c are divided into demographic bucketprobabilities 406 a-406 m. The demographic bucket probabilities 406a-406 m define the probability that the real age of the subscriber fallswithin the corresponding demographic bucket. When the example agepredictor 304 (FIG. 3) uses the example age prediction model 306 (FIG.3) based on the user subscriber activity metrics 138, the age predictionmodel 306 outputs one of the age PDFs 305 a-305 c and/or age PDF IDs404. For example, if the age prediction model 306 assigns a firstsubscriber to an age PDF 305 b identified by the age PDF ID 404 “A2,”the probability that the real age of the first subscribe is 12-14 is100%. As another example, if the age prediction model 306 assigns asecond subscriber to an age PDF 305 c identified by the age PDF ID 404“A3,” the probability of the real age of the second subscriber being2-11 is 6.2%, the probability of the real age of the second subscriberbeing 12-14 is 32.3%, the probability of the real age of the secondsubscriber being 15-17 is 60%, and the probability of the real age ofthe second subscriber being 35-39 is 1.5%.

FIG. 5 illustrates an example data structure 500 to store age PDF(s)(e.g., the age PDF(s) 305 of FIGS. 3 and 4) predicted by age predictionmodel 306 (FIG. 3). In the illustrated example, the data structure 500includes an example user identifier 130, an example age PDF identifier(age PDF ID) 404, and an example prediction date 502. The example useridentifier 130 is used by the age updater 310 (FIG. 3) to retrieve theage PDF ID 404 and the prediction data 502 when an age request 128(FIGS. 1 and 2) is received. The example user identifier 130 may be theuser/device identifier 114 (FIG. 1), the anonymized identifier, and/orany other identifying value that remains a consistent identifier for asubscriber between the prediction date 502 and the impression date 302(FIG. 3). The age PDF ID 404 identifies an age PDF that was predictedfor the subscriber based on subscriber activity metrics 138 (FIG. 1) onthe prediction date 502.

In the illustrated example of FIG. 3, the example age updater 310retrieves the age PDF ID 404 (FIG. 4) and the prediction date 502 (FIG.5) from the age cache 308 upon receiving an age request 128. In theillustrated example, the age updater 310 probabilistically ages the agePDF retrieved from the age cache 308 to produce an age-corrected PDF134. To probabilistically age the age PDF, the age updater 310 calculateaging factor(s) (AF) in accordance with Equation 2 below.

$\begin{matrix}{{AF}_{j} = \frac{{NumDays}( {D_{I} - D_{P}} )}{{NumDays}( D_{j} )}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

In Equation 2 above, AF_(j) is an aging factor for the demographicbucket j (e.g., one of the demographic buckets 406 a-406 m of FIG. 4),D_(I) is the impression date 302, D_(P) is the prediction date 502,NumDays(D_(I)−D_(P)) is the number of days between the impression date302 and the prediction date 502, and NumDays(D_(j)) is the number ofdays that are spanned by the demographic bucket j. For example, athree-year spanning demographic bucket (e.g., an age 18-20 demographicage bucket, etc.) has 1096 days. For example, if the impression date 302is Dec. 5, 2014, the prediction date 502 is Apr. 17, 2014, and the spanof the demographic bucket is three years, the aging factor would be 0.21(232/1096) because the number of days between Apr. 17, 2014 and Dec. 5,2014 is 232. In some examples, where the age ranges of the demographicbuckets are the same, the age updater 310 only calculates a single agingfactor. For example, if the demographic buckets each span five years(e.g., demographic bucket A spans ages 7-11, demographic bucket B spansages 12-16, demographic bucket C spans ages 17-21, etc.), the agefactors would be identical (e.g., NumDays(D_(j)) would be 1826)

To probabilistically age the age PDF corresponding to the age PDF ID 404retrieved from the age cache 308, the age updater 310 applies the agingfactor(s) to the age PDF in accordance with Equation 3 below.

ACPDF=APDF×M   Equation 3

In Equation 3 above, ACPDF is the age-corrected PDF 134, APDF is a 1×Nmatric formed by the age PDF retrieved from the age cache 308, where Nis the number of demographic buckets in the age PDF, and M is an agingfactor matrix calculated in accordance with Equation 4 below.

$\begin{matrix}{M = \begin{pmatrix}{1 - {AF}_{1}} & {AF}_{1} & 0 & \ldots & 0 & 0 \\0 & {1 - {AF}_{2}} & {AF}_{2} & \ldots & 0 & 0 \\\vdots & \vdots & \vdots & \ddots & \vdots & \vdots \\0 & 0 & 0 & \ldots & {1 - {AF}_{j - 1}} & {AF}_{j - 1} \\0 & 0 & 0 & \ldots & 0 & 1\end{pmatrix}} & {{Equation}\mspace{14mu} 4}\end{matrix}$

In Equation 4 above, AF_(j) is the aging factor for demographic bucketj.

Consider the following example. An age request 128 is received by theage updater 310 with a UID 130 of “8PR6PYRGQC” and an impression date302 of Oct. 10, 2014. The age updater 310 retrieves a record from theage cache 308 that has an age PDF ID 404 of “A7” and a prediction date502 of Jun. 9, 2014. The age PDF ID 404 “A7” corresponds to an age PDFas shown in Table 3 below.

TABLE 3 EXAMPLE AGE PDF Demographic Bucket 13-17 18-21 22-27 28-34 34-40Probability 0% 15% 53% 32% 0%Using Equation 2 above, that age updater 310 calculates the agingfactors as shown below in Table 4.

TABLE 4 EXAMPLE AGING FACTORS BASED ON THE EXAMPLE AGE PDF OF TABLE 3Demographic Bucket 1 2 3 4 5 Aging Factor 0.068 0.085 0.057 0.049 0.049A matrix, M, applying Equation 4 above to the aging factors in Table 4above is shown in Equation 5 below.

$\begin{matrix}{M = {\begin{pmatrix}0.932 & 0.068 & 0 & 0 & 0 \\0 & 0.915 & 0.085 & 0 & 0 \\0 & 0 & 0.943 & 0.057 & 0 \\0 & 0 & 0 & 0.951 & 0.049 \\0 & 0 & 0 & 0 & 1\end{pmatrix}.}} & {{Equation}\mspace{14mu} 5}\end{matrix}$

Applying Equation 3 to Equation 5 above and the example age PDF of Table3 above, the age-corrected PDF 134 determined by the example age updater310 is shown in Table 5 below.

TABLE 5 EXAMPLE AGE-CORRECTED PDF Demographic Bucket 13-17 18-21 22-2728-34 34-40 Probability 0% 14% 51% 33% 2%

While an example manner of implementing the example age corrector 124 ofFIGS. 1 and/or 2 is illustrated in FIG. 3, one or more of the elements,processes and/or devices illustrated in FIG. 3 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example age predictor 304, the age prediction model 306,the age cache 308, the example age updater 310, the example modelcorrector 312 and/or, more generally, the example age corrector 124 ofFIG. 3 may be implemented by hardware, software, firmware and/or anycombination of hardware, software and/or firmware. Thus, for example,any of the example age predictor 304, the age prediction model 306, theage cache 308, the example age updater 310, the example model corrector312 and/or, more generally, the example age corrector 124 could beimplemented by one or more analog or digital circuit(s), logic circuits,programmable processor(s), application specific integrated circuit(s)(ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)). When reading any of theapparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example agepredictor 304, the age prediction model 306, the age cache 308, theexample age updater 310, the example model corrector 312, and/or theexample age corrector 124 is/are hereby expressly defined to include atangible computer readable storage device or storage disk such as amemory, a digital versatile disk (DVD), a compact disk (CD), a Blu-raydisk, etc. storing the software and/or firmware. Further still, theexample age corrector 124 of FIGS. 1 and 2 may include one or moreelements, processes and/or devices in addition to, or instead of, thoseillustrated in FIG. 3, and/or may include more than one of any or all ofthe illustrated elements, processes and devices.

FIG.6 illustrates an example communication flow diagram of an examplemanner in which the AME 108 and the database proprietor 110 can collectimpressions and demographic information based on a client device 104reporting impressions to the AME 108 and the database proprietor 110.Examples disclosed herein to predict age may be used in connection withdemographic impressions collected using example techniques describedbelow in connection with FIG. 6. FIG. 6 also shows example agecorrectors 124. In some examples, the age corrector 124 is part of thedatabase proprietor 110 as shown in FIG. 1. In some examples, the agecorrect 124 is part of AME 108 as shown in FIG. 2. The example chain ofevents shown in FIG. 6 occurs when the client device 104 accesses mediafor which the client device 104 reports an impression to the AME 108 andthe database proprietor 110. In some examples, the client device 104reports impressions for accessed media based on instructions (e.g.,beacon instructions) embedded in the media that instruct the clientdevice 104 (e.g., instruct a web browser or an app in the client device104) to send beacon/impression requests 608 to the AME 108 and/or thedatabase proprietor 110. In such examples, the media having the beaconinstructions is referred to as tagged media. In other examples, theclient device 104 reports impressions for accessed media based oninstructions embedded in apps or web browsers that execute on the clientdevice 104 to send beacon/impression requests 608 to the AME 108, and/orthe database proprietor 110 for corresponding media accessed via thoseapps or web browsers. In any case, the beacon/impression requests 608include device/user identifiers 114 as described further below to allowthe corresponding AME 108 and/or database proprietor 110 to associatedemographic information with resulting logged impressions.

In the illustrated example, the client device 104 accesses media 606that is tagged with beacon instructions 608. The beacon instructions 608cause the client device 104 to send a beacon/impression request 612 toan AME impressions collector 618 when the client device 104 accesses themedia 606. For example, a web browser and/or app of the client device104 executes the beacon instructions 608 in the media 606 which instructthe browser and/or app to generate and send the beacon/impressionrequest 612. In the illustrated example, the client device 104 sends thebeacon/impression request 612 to the AME impression collector 618 usingan HTTP (hypertext transfer protocol) request addressed to the URL(uniform resource locator) of the AME impressions collector 618 at, forexample, a first internet domain of the AME 108. The beacon/impressionrequest 612 of the illustrated example includes a media identifier 613(e.g., an identifier that can be used to identify content, anadvertisement, and/or any other media) corresponding to the media 606.In some examples, the beacon/impression request 612 also includes a siteidentifier (e.g., a URL) of the website that served the media 606 to theclient device 104 and/or a host website ID (e.g., www.acme.com) of thewebsite that displays or presents the media 606. In the illustratedexample, the beacon/impression request 612 includes a device/useridentifier 114. In the illustrated example, the device/user identifier114 that the client device 104 provides in the beacon impression request612 is an AME ID (e.g., a cookie or other identifier). The AME IDcorresponds to an identifier that the AME 108 uses to identify apanelist corresponding to the client device 104. In other examples, theclient device 104 may not send the device/user identifier 114 until theclient device 104 receives a request for the same from a server of theAME 108 (e.g., in response to, for example, the AME impressionscollector 618 receiving the beacon/impression request 612).

In some examples, the device/user identifier 114 may be a deviceidentifier (e.g., an international mobile equipment identity (IMEI), amobile equipment identifier (MEID), a media access control (MAC)address, etc.), a web browser unique identifier (e.g., a cookie), a useridentifier (e.g., a user name, a login ID, etc.), an Adobe Flash® clientidentifier, identification information stored in an HTML5 datastore, avehicle identification number (VIN) and/or any other identifier that theAME 108 stores in association with demographic information about usersof the client devices 104. When the AME 108 receives the device/useridentifier 114, the AME 108 can obtain demographic informationcorresponding to a user of the client device 104 based on thedevice/user identifier 114 that the AME 108 receives from the clientdevice 104. In some examples, the device/user identifier 114 may beencrypted (e.g., hashed) at the client device 104 so that only anintended final recipient of the device/user identifier 114 can decryptthe hashed identifier 114. For example, if the device/user identifier114 is a cookie that is set in the client device 104 by the AME 108, thedevice/user identifier 114 can be hashed so that only the AME 108 candecrypt the device/user identifier 114. If the device/user identifier114 is an IMEI number, the client device 104 can hash the device/useridentifier 114 so that only a wireless carrier (e.g., the databaseproprietor 110) can decrypt the hashed identifier 114 to recover theIMEI for use in accessing demographic information corresponding to theuser of the client device 104. By hashing the device/user identifier114, an intermediate party (e.g., an intermediate server or entity onthe Internet) receiving the beacon request cannot directly identify auser of the client device 104.

In response to receiving the beacon/impression request 612, the AMEimpressions collector 618 logs an impression for the media 606 bystoring the media identifier 613 contained in the beacon/impressionrequest 612. In the illustrated example of FIG. 6, the AME impressionscollector 618 also uses the device/user identifier 114 in thebeacon/impression request 612 to identify AME panelist demographicinformation corresponding to a panelist of the client device 104. Thatis, the device/user identifier 114 matches a user ID of a panelistmember (e.g., a panelist corresponding to a panelist profile maintainedand/or stored by the AME 108). In this manner, the AME impressionscollector 618 can associate the logged impression with demographicinformation of a panelist corresponding to the client device 104.

In some examples, the beacon/impression request 612 may not include thedevice/user identifier 114 if, for example, the user of the clientdevice 104 is not an AME panelist. In such examples, the AME impressionscollector 618 logs impressions regardless of whether the client device104 provides the device/user identifier 114 in the beacon/impressionrequest 612 (or in response to a request for the identifier 114). Whenthe client device 104 does not provide the device/user identifier 114,the AME impressions collector 618 will still benefit from logging animpression for the media 606 even though it will not have correspondingdemographics. For example, the AME 108 may still use the loggedimpression to generate a total impressions count and/or a frequency ofimpressions (e.g., an impressions frequency) for the media 606.Additionally or alternatively, the AME 108 may obtain demographicsinformation from the database proprietor 110 for the logged impressionif the client device 104 corresponds to a subscriber of the databaseproprietor 110.

In the illustrated example of FIG. 6 to compare or supplement panelistdemographics (e.g., for accuracy or completeness) of the AME 108 withdemographics from one or more database proprietors (e.g., the databaseproprietor 110), the AME impressions collector 618 returns a beaconresponse message 622 (e.g., a first beacon response) to the clientdevice 104 including an HTTP “302 Found” re-direct message and a URL ofa participating database proprietor 110 at, for example, a secondinternet domain. In the illustrated example, the HTTP “302 Found”re-direct message in the beacon response 622 instructs the client device104 to send a second beacon request 626 to the database proprietor 110.In other examples, instead of using an HTTP “302 Found” re-directmessage, redirects may be implemented using, for example, an iframesource instruction (e.g., <iframe src=“ ”>) or any other instructionthat can instruct a client device to send a subsequent beacon request(e.g., the second beacon request 626) to a participating databaseproprietor 110. In the illustrated example, the AME impressionscollector 618 determines the database proprietor 110 specified in thebeacon response 622 using a rule and/or any other suitable type ofselection criteria or process. In some examples, the AME impressionscollector 618 determines a particular database proprietor to which toredirect a beacon request based on, for example, empirical dataindicative of which database proprietor is most likely to havedemographic data for a user corresponding to the device/user identifier114. In some examples, the beacon instructions 608 include a predefinedURL of one or more database proprietors to which the client device 104should send follow up beacon requests 626. In other examples, the samedatabase proprietor is always identified in the first redirect message(e.g., the beacon response 622).

In some examples, the AME 108 assigns an identifier to the device 104 inresponse to receiving a first message from the device 104. For example,the AME 108 may send an AME cookie on the device 104 with the beaconresponse 622. When the device 104 sends subsequent beacon requests 612,the device 104 may include the AME cookie to enable to the AME 108 toidentify that the device 104 has already been observed. In some cases,the AME cookie expires after a valid time period of the cookie, afterwhich a new AME cookie is set by the AME 108 in response to a beaconrequest 612 from the same device 104.

In the illustrated example of FIG. 6, the beacon/impression request 626may include a device/user identifier 114 that is a database proprietorID because it is used by the database proprietor 110 to identify asubscriber of the client device 104 when logging an impression. In someinstances (e.g., in which the database proprietor 110 has not yet set adatabase proprietor ID in the client device 104), the beacon/impressionrequest 626 does not include the device/user identifier 114. In someexamples, the database proprietor ID is not sent until the databaseproprietor 110 requests the same (e.g., in response to thebeacon/impression request 626). When the database proprietor 110receives the device/user identifier 114, the database proprietor 110 canobtain demographic information corresponding to a user of the clientdevice 104 based on the device/user identifier 114 that the databaseproprietor 110 receives from the client device 104. In some examples,the device/user identifier 114 may be encrypted (e.g., hashed) at theclient device 104 so that only an intended final recipient of thedevice/user identifier 114 can decrypt the hashed identifier 114. Forexample, if the device/user identifier 114 is a cookie that is set inthe client device 104 by the database proprietor 110, the device/useridentifier 114 can be hashed so that only the database proprietor 110can decrypt the device/user identifier 114. If the device/useridentifier 114 is an IMEI number, the client device 104 can hash thedevice/user identifier 114 so that only a wireless carrier (e.g., thedatabase proprietor 110) can decrypt the hashed identifier 114 torecover the IMEI for use in accessing demographic informationcorresponding to the user of the client device 104. By hashing thedevice/user identifier 114, an intermediate party (e.g., an intermediateserver or entity on the Internet) receiving the beacon request cannotdirectly identify a user of the client device 104. For example, if theintended final recipient of the device/user identifier 114 is thedatabase proprietor 110, the AME 108 cannot recover identifierinformation when the device/user identifier 114 is hashed by the clientdevice 104 for decrypting only by the intended database proprietor 110.

In some examples that use cookies as the device/user identifier 114,when a user deletes a database proprietor cookie from the client device104, the database proprietor 110 sets the same cookie value in theclient device 104 the next time the user logs into a service of thedatabase proprietor 110. In such examples, the cookies used by thedatabase proprietor 110 are registration-based cookies, which facilitatesetting the same cookie value after a deletion of the cookie value hasoccurred on the client device 104. In this manner, the databaseproprietor 110 can collect impressions for the client device 104 basedon the same cookie value over time to generate unique audience (UA)sizes while eliminating or substantially reducing the likelihood that asingle unique person will be counted as two or more separate uniqueaudience members.

Although only a single database proprietor 110 is shown in FIG. 6, theimpression reporting/collection process of FIG. 6 may be implementedusing multiple database proprietors 110. In some such examples, thebeacon instructions 608 cause the client device 104 to sendbeacon/impression requests 626 to numerous database proprietors 110. Forexample, the beacon instructions 608 may cause the client device 104 tosend the beacon/impression requests 626 to the numerous databaseproprietors 110 in parallel or in daisy chain fashion. In some suchexamples, the beacon instructions 608 cause the client device 104 tostop sending beacon/impression requests 626 to database proprietors oncea database proprietor has recognized the client device 104. In otherexamples, the beacon instructions 608 cause the client device 104 tosend beacon/impression requests 626 to database proprietors 110 so thatmultiple database proprietors 110 can recognize the client device 104and log a corresponding impression. In any case, multiple databaseproprietors 110 are provided the opportunity to log impressions andprovide corresponding demographics information if the user of the clientdevice 104 is a subscriber of services of those database proprietors.

In some examples, prior to sending the beacon response 622 to the clientdevice 101, the AME impressions collector 618 replaces site IDs (e.g.,URLs) of media provider(s) that served the media 606 with modified siteIDs (e.g., substitute site IDs) which are discernable only by the AME108 to identify the media provider(s). In some examples, the AMEimpressions collector 618 may also replace a host website ID (e.g.,www.acme.com) with a modified host site ID (e.g., a substitute host siteID) which is discernable only by the AME 108 as corresponding to thehost website via which the media 606 is presented. In some examples, theAME impressions collector 618 also replaces the media identifier 613with a modified media identifier 613 corresponding to the media 606. Inthis way, the media provider of the media 606, the host website thatpresents the media 606, and/or the media identifier 613 are obscuredfrom the database proprietor 110, but the database proprietor 110 canstill log impressions based on the modified values which can later bedeciphered by the AME 108 after the AME 108 receives logged impressionsfrom the database proprietor 110. In some examples, the AME impressionscollector 618 does not send site IDs, host site IDS, the mediaidentifier 613 or modified versions thereof in the beacon response 622.In such examples, the client device 104 provides the original,non-modified versions of the media identifier 613, site IDs, host IDs,etc. to the database proprietor 110.

In the illustrated example, the AME impression collector 618 maintains amodified ID mapping table 628 that maps original site IDs with modified(or substitute) site IDs, original host site IDs with modified host siteIDs, and/or maps modified media identifiers to the media identifierssuch as the media identifier 613 to obfuscate or hide such informationfrom database proprietors such as the database proprietor 110. Also inthe illustrated example, the AME impressions collector 618 encrypts allof the information received in the beacon/impression request 612 and themodified information to prevent any intercepting parties from decodingthe information. The AME impressions collector 618 of the illustratedexample sends the encrypted information in the beacon response 622 tothe client device 104 so that the client device 104 can send theencrypted information to the database proprietor 110 in thebeacon/impression request 626. In the illustrated example, the AMEimpressions collector 618 uses an encryption that can be decrypted bythe database proprietor 110 site specified in the HTTP “302 Found”re-direct message.

From time to time, the impression data collected by the databaseproprietor 110 is provided to a database proprietor impressionscollector 630 of the AME 108 as, for example, batch (e.g., aggregate)data. As discussed above, some impressions logged by the client device104 to the database proprietor 110 are misattributed by the databaseproprietor 110 to a wrong subscriber and, thus, to incorrect demographicinformation. During a data collecting and merging process to combinedemographic and impression data from the AME 108 and the databaseproprietor 110, demographics of impressions logged by the AME 108 forthe client device 104 will not correspond to demographics of impressionslogged by the database proprietor 110 because the database proprietor110 has misattributed some impressions to the incorrect demographicinformation. Examples disclosed herein may be used to determinecorrected age-based demographic groups of impression data provided bythe database proprietor 110.

The example AME 108 of FIG. 6 further includes a ratings determiner 632.The example ratings determiner 632 generates ratings information for themedia by attributing impressions, duration (e.g., viewing or listeningminutes), and/or audience counts to demographic groups (e.g., genderand/or age groups). The ratings information generated by the ratingsdeterminer 632 is based on the ages predicted by the age corrector 124,where the ages are corrected for deterioration of an age predictionmodel.

Additional examples that may be used to implement the beacon instructionprocesses of FIG. 6 are disclosed in U.S. Pat. No. 8,370,489, entitled“Methods and Apparatus to Determine Impressions Using DistributedDemographic Information,” which is hereby incorporated herein byreference in its entirety. In addition, other examples that may be usedto implement such beacon instructions are disclosed in U.S. Pat. No.6,108,637, entitled “Content Display Monitor,” which is herebyincorporated herein by reference in its entirety.

In some examples, the database proprietor 110, before providingaggregated demographics to the AME 108, uses the age corrector 124 tocorrect age-based demographic group misattributions in the impressiondata. In some examples, the aggregate impressions data includes anaggregate age PDF. In some examples, when the AME 108 receivesanonymized user-level impression data and/or demographic data from thedatabase proprietor 110, the AME 108 uses the age corrector 124 tocorrect age-based demographic group misattributions in the impressiondata.

A flowchart representative of example machine readable instructions forimplementing the age corrector 124 of FIG. 3 is shown in FIG. 7. Aflowchart representative of example machine readable instructions forimplementing the age updater 310 of FIG. 3 is shown in FIG. 8. Aflowchart representative of example machine readable instructions forimplementing the demographic impression aggregator 126 of FIG. 1 isshown in FIG. 9. In this example, the machine readable instructionscomprise a program for execution by a processor such as the processor1012 shown in the example processor platform 1000 discussed below inconnection with FIG. 10. The program may be embodied in software storedon a tangible computer readable storage medium such as a CD-ROM, afloppy disk, a hard drive, a digital versatile disk (DVD), a Blu-raydisk, or a memory associated with the processor 1012, but the entireprogram and/or parts thereof could alternatively be executed by a deviceother than the processor 1012 and/or embodied in firmware or dedicatedhardware. Further, although the example programs are described withreference to the flowcharts illustrated in FIGS. 7, 8, 9, and/or 13,many other methods of implementing the example age corrector 124, theexample age updater 310, the example model corrector 312, and/or theexample demographic impression aggregator 126 may alternatively be used.For example, the order of execution of the blocks may be changed, and/orsome of the blocks described may be changed, eliminated, or combined.

As mentioned above, the example processes of FIGS. 7, 8, 9, and/or 13may be implemented using coded instructions (e.g., computer and/ormachine readable instructions) stored on a tangible computer readablestorage medium such as a hard disk drive, a flash memory, a read-onlymemory (ROM), a compact disk (CD), a digital versatile disk (DVD), acache, a random-access memory (RAM) and/or any other storage device orstorage disk in which information is stored for any duration (e.g., forextended time periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, “tangible computer readable storage medium” and “tangiblemachine readable storage medium” are used interchangeably. Additionallyor alternatively, the example processes of FIGS. 7, 8, 9, and/or 13 maybe implemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a non-transitory computer and/ormachine readable medium such as a hard disk drive, a flash memory, aread-only memory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the term“comprising” is open ended.

FIG. 7 is a flow diagram of example machine readable instructions 700that may be executed to implement the data corrector 124 of FIGS. 1, 2,and/or 5 to correct ages associated with demographic impressionsassociated with subscribers of a database proprietor 110. Initially, atblock 702, the age predictor 304 (FIG. 3) obtains subscriber data (e.g.,the subscriber data 136 of FIGS. 1, 2, and 3). The example subscriberdata 136 includes an identifier (e.g., UID 130 of FIGS. 1, 2, and 3) andsubscriber activity metrics (e.g., the subscriber activity metrics 138of FIGS. 1, 2, and 3) for one or more subscribers of the databaseproprietor 110. In some examples, the subscriber data 136 is obtained inresponse to an age prediction model 306 (FIG. 3) being generated so thatthe age predictor 304 can make age predictions based on the subscriberdata 136 soon after the age prediction model 306 is generated. In someexamples, the age predictor 304 retrieves subscriber data 136 for asubset of the subscribers of the database proprietor 110. For example,the age predictor 304 may retrieve subscriber data 136 of subscribersthat have subscribed to the database proprietor 110 since the ageprediction model 306 was last generated.

At block 704, the age predictor 304 obtains an age PDF (e.g., the agePDF 305 of FIG. 3) from the prediction model 306 using the subscriberactivity metrics 138. In some examples, using the subscriber activitymetrics 138 as inputs into the age prediction model 306, theintermediate nodes of the age prediction model 306 are traversed untilthe age prediction model 306 reaches a terminal node. In such anexample, the terminal node identifies an age PDF (e.g., the age PDF 305of FIGS. 3 and 4) and/or an age PDF ID (e.g., the age PDF ID 404 ofFIGS. 4 and 5). In some examples, the age PDF 305 is identified by theage PDF ID. At block 706, the age predictor 304 stores the predicted agePDF 305 and/or the age PDF ID 404, the UID 130 included in thesubscriber data 138, and the date the prediction was made (e.g., theprediction date 502 of FIG. 5) in the age cache 308. At block 708, theage predictor 304 determines whether there is another subscriber (e.g.,more subscriber data 136) for which to obtain a predicted age PDF. Ifthere is another subscriber, program control returns to block 704.Otherwise, if there is not another subscriber, program control advancesto block 710.

At block 710, the age updater 310 waits until an age request (e.g., theage request 128 of FIGS. 1 and 2) has been received. For example, theage updater 310 may receive the age request 128 from the impressionhandler 120 (FIG. 1) or the age adjuster 206 (FIG. 2) to determine theage-corrected PDF 134 at a later time (e.g., days, weeks, months, etc.)after the age predictor 304 determines an initial predicted age PDF 305represented in the age cache 308. If an age request 128 has beenreceived, program control advances to block 712. At block 712, the ageupdater 310 retrieves a predicted age PDF 305 and/or an age PDF ID 404and a prediction date 502 from the age cache 308 based on a UID 130included in the age request 128. At block 714, the age updater 310calculates an age-corrected PDF 134 (FIGS. 1, 2 and 3). An exampleprocess for implementing the operation of block 714 is described belowin connection with FIG. 8. At block 716, the age updater 310 determinesif there is another age request 128. If there is another age request128, program control returns to block 710. Otherwise, if there is notanother age request 128, the example program 700 ends.

FIG. 8 is a flow diagram of example machine readable instructions 714that may be executed to implement the age updater 310 of FIG. 3 tocalculate an age-corrected PDF 134 (FIGS. 1, 2 and 3) based on apredicted age PDF with a corresponding prediction date 502 (FIG. 5) andan age request 128 (FIGS. 1, 2, and 3) that includes an impression date302. The example process of FIG. 8 may be used to implement clock 714 ofFIG. 7. Initially, at block 800, the age updater 310 selects ademographic bucket from the predicted age PDF for which to calculate anaging factor. For example, if the predicted age PDF (e.g., the predictedage PDFs 305 a-305 c of FIG. 4) has a 2-11 year-old demographic bucket,a 12-14 year-old demographic bucket, a 15-17 year-old demographicbucket, an 18-20 year-old demographic bucket, a 21-24 year-olddemographic bucket, a 25-29 year-old demographic bucket, a 30-34year-old demographic bucket, a 35-39 year-old demographic bucket, a40-44 year-old demographic bucket, a 45-49 year-old demographic bucket,a 50-54 year-old demographic bucket, a 55-64 year-old demographicbucket, and a 65+ year-old demographic bucket, the age updater 310 mayselect the 2-11 year-old demographic bucket. At block 802, the ageupdater 310 calculates an aging factor for the selected demographicbucket. In some examples, the aging factors are calculated in accordancewith Equation 2 above. For example, if the impression date 302 is Sep.25, 2014, the prediction date 502 is Mar. 2, 2014, and the span of theselected demographic bucket is five years (e.g., an ages 2-6 year-olddemographic bucket, an ages 7-11 year-old demographic bucket, etc.), theaging factor is 0.114 (208/1826) because 208 is the number of daysbetween Sep. 25, 2014, and Mar. 2, 2014, and 1826 is the number of daysin five years.

At block 804, the age updater 310 determines whether another agingfactor is to be calculated for another demographic bucket. For example,another aging factor is to be calculated if one or more demographicbuckets of a predicted age PDF have not had corresponding aging factorscalculated. If another aging factor is to be calculated for anotherdemographic bucket, program control returns to block 800. Otherwise, ifanother aging factor is not to be calculated for another demographicbucket, program control advances to block 806. At block 806, the ageupdater 310 applies the aging factor(s) calculated at block 802 to thepredicted age PDF. For example, the age updater 310 applies agingfactors to the predicted age PDF to generate the age-corrected PDF 134(FIGS. 1, 2, and 3). In some examples, the aging factors are applied tothe predicted age PDF in accordance with Equation 3 and Equation 4above. Example program 714 then ends.

FIG. 9 is a flow diagram of example machine readable instructions 900that may be executed to implement the demographic impression aggregator126 of FIG. 1 to calculate aggregated age-corrected PDFs to include withaggregate demographic impressions 106 (FIG. 1). The example process ofFIG. 9 may be used when aggregate demographic impressions 106 are to besent to the AME 108. Initially, at block 902, the demographic impressionaggregator 126 selects characteristics (e.g., gender, marital status,media, device type, location, etc.) to include and/or exclude togenerate aggregate demographic impressions 106. For example, thedemographic impression aggregator 126 may select “males, ‘Ricky andMorty’, New England” (e.g., a gender, a media presentation, and ageographical region) as the characteristics to use to aggregate thedemographic impressions. In some examples, the selected characteristicsare based on agreements between the AME 108 (FIG. 1) and the databaseproprietor 110 (FIG. 1). In some examples, the selected characteristicsare based on requests for aggregate demographic data 106 by the AME 108.At block 904, the demographic impression aggregator 126 retrieves thedemographic impressions corresponding to the characteristics selected atblock 902 from the impression database 116.

At block 906, the demographic impression aggregator 126 calculates anaggregate age-corrected PDF based on the age-corrected PDFscorresponding to the demographic impressions retrieved at block 904. Insome examples, to calculate an aggregate age-corrected PDF, thedemographic impression aggregator 126 averages the probabilities foreach demographic bucket. For example, if the probabilities associatedwith the 18-20 year-old demographic bucket for predicted age PDFs to beaggregated are 13%, 20%, 5% and 10%, the aggregate probability for the18-20 year-old demographic bucket would be 12% ((13%+20%+5%+10%)/4). Insome examples, the aggregate age-corrected PDF is calculated inaccordance with Equation 1 above. At block 908, the demographicimpression aggregator 126 generates the aggregate demographic impressiondata 106. In some examples, the demographic impression aggregator 126generates the aggregate demographic impression data 106 by associatingthe aggregate age-corrected PDF calculated at block 906 with a count ofthe demographic impressions retrieved at block 904. Example program 900then ends.

FIG. 10 is a block diagram of an example processor platform 1100structured to execute the instructions of FIGS. 7, 8, 9, and/or 13 toimplement the age corrector 124, the impression handler 120, thedemographic impression aggregator 126, the impressions collectors 204,the age adjuster 206, the age predictor 304, the age prediction model306, the age cache 308, the age updater 310, and/or the model corrector312 of FIGS. 1, 2, 3 and/or 6. By way of example, FIG. 10 shows the agepredictor 304 and the age updater 310, but processor platform 1000 mayinclude more of, fewer of, or different ones of the age corrector 124,the impression handler 120, the demographic impression aggregator 126,the impressions collectors 204, the age adjuster 206, the age predictor304, the age prediction model 306, the age cache 308, the age updater310, and/or the model corrector 312 of FIGS. 1, 2, 3, and/or 6. Theprocessor platform 1000 can be, for example, a server, a personalcomputer, a workstation, or any other type of computing device.

The processor platform 1000 of the illustrated example includes aprocessor 1012. The processor 1012 of the illustrated example ishardware. For example, the processor 1012 can be implemented by one ormore integrated circuits, logic circuits, microprocessors or controllersfrom any desired family or manufacturer.

The processor 1012 of the illustrated example includes a local memory1013 (e.g., a cache). The processor 1012 of the illustrated example isin communication with a main memory including a volatile memory 1014 anda non-volatile memory 1016 via a bus 1018. The volatile memory 1014 maybe implemented by Synchronous Dynamic Random Access Memory (SDRAM),Dynamic Random Access Memory (DRAM), RAIVIBUS Dynamic Random AccessMemory (RDRAM) and/or any other type of random access memory device. Thenon-volatile memory 1016 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 1014,1016 is controlled by a memory controller.

The processor platform 1000 of the illustrated example also includes aninterface circuit 1020. The interface circuit 1020 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 1022 are connectedto the interface circuit 1020. The input device(s) 1022 permit(s) a userto enter data and commands into the processor 1012. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 1024 are also connected to the interfacecircuit 1020 of the illustrated example. The output devices 1124 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a printer and/or speakers). The interface circuit 1020 ofthe illustrated example, thus, typically includes a graphics drivercard, a graphics driver chip or a graphics driver processor.

The interface circuit 1020 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network1026 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 1000 of the illustrated example also includes oneor more mass storage devices 1028 for storing software and/or data.Examples of such mass storage devices 1028 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

Coded instructions 1032 to implement the machine readable instructionsof FIGS. 7, 8, 9, and/or 13 may be stored in the mass storage device1028, in the volatile memory 1014, in the non-volatile memory 1016,and/or on a removable tangible computer readable storage medium such asa CD or DVD.

FIG. 11 depicts an example system 1100 to collect user information(e.g., user information 1102 a, 1102 b) from distributed databaseproprietors 110 a, 110 b for associating with impressions of mediapresented at a client device 104. Examples disclosed herein to predictages may be used in connection with demographic impressions collectedusing example techniques described below in connection with FIG. 11.

In the illustrated examples, user information 1102 a, 1102 b or userdata includes one or more of demographic data, purchase data, and/orother data indicative of user activities, behaviors, and/or preferencesrelated to information accessed via the Internet, purchases, mediaaccessed on electronic devices, physical locations (e.g., retail orcommercial establishments, restaurants, venues, etc.) visited by users,etc. In some examples, user information 1102 a, 1102 b is stored in thesubscriber accounts database 122 of FIG. 1. Examples of FIG. 11 aredescribed in connection with a mobile device, which may be a mobilephone, a mobile communication device, a tablet, a gaming device, aportable media presentation device, an in-vehicle or vehicle-integratedcommunication system, such as an automobile infotainment system withwireless communication capabilities, etc. However, examples disclosedherein may be implemented in connection with impressions correspondingto mobile and/or non-mobile devices. Example non-mobile devices includeinternet appliances, smart televisions, internet terminals, computers,or any other device capable of presenting media received via networkcommunications.

In the illustrated example of FIG. 11, to track media impressions on theclient device 104, the AME 108 partners with or cooperates with an apppublisher 1110 to download and install a data collector 1112 on theclient device 104. The app publisher 1110 of the illustrated example maybe a software app developer that develops and distributes apps to mobiledevices and/or a distributor that receives apps from software appdevelopers and distributes the apps to mobile devices. The datacollector 1112 may be included in other software loaded onto the clientdevice 104, such as the operating system 1114, an application (or app)1116, a web browser 1117, and/or any other software. In some examples,the example client device 104 of FIG. 11 is a non-locally metereddevice. For example, the client device 104 of a non-panelist householddoes not support and/or has not been provided with specific meteringsoftware (e.g., dedicated metering software provided directly by the AME108 and executing as a foreground or background process for the solepurpose of monitoring media accesses/exposure).

Any of the example software 1114-1117 may present media 1118 receivedfrom a media publisher 1120. The media 1118 may be an advertisement,video, audio, text, a graphic, a web page, news, educational media,entertainment media, or any other type of media. In the illustratedexample, a media ID 1122 is provided in the media 1118 to enableidentifying the media 1118 so that the AME 108 can credit the media 1118with media impressions when the media 1118 is presented on the clientdevice 104 or any other device that is monitored by the AME 108.

The data collector 1112 of the illustrated example includes instructions(e.g., Java, java script, or any other computer language or script)that, when executed by the client device 104, cause the client device104 to collect the media ID 1122 of the media 1118 presented by the appprogram 1116 and/or the client device 104, and to collect one or moredevice/user identifier(s) 114 stored in the client device 104. Thedevice/user identifier(s) 114 of the illustrated example includeidentifiers that can be used by corresponding ones of the partnerdatabase proprietors 110 a, 110 b to identify the user or users of theclient device 104, and to locate user information 1102 a, 1102 bcorresponding to the user(s). For example, the device/user identifier(s)114 used in connection with examples of FIG. 11 may include hardwareidentifiers (e.g., an international mobile equipment identity (IMEI), amobile equipment identifier (MEID), a media access control (MAC)address, etc.), an app store identifier (e.g., a Google Android ID, anApple ID, an Amazon ID, etc.), an open source unique device identifier(OpenUDID), an open device identification number (ODIN), a loginidentifier (e.g., a username), an email address, user agent data (e.g.,application type, operating system, software vendor, software revision,etc.), third-party service identifiers (e.g., advertising serviceidentifiers, device usage analytics service identifiers, demographicscollection service identifiers), web storage data, document object model(DOM) storage data, local shared objects (also referred to as “Flashcookies”), an automobile vehicle identification number (VIN), etc. Insome examples, fewer or more device/user identifier(s) 114 may be used.In addition, although only two partner database proprietors 110 a, 110 bare shown in FIG. 11, the AME 108 may partner with any number of partnerdatabase proprietors to collect distributed user information (e.g., theuser information 1102 a, 1102 b).

In some examples, the client device 104 may not allow access toidentification information stored in the client device 104. For suchinstances, the disclosed examples enable the AME 108 to store anAME-provided identifier (e.g., an identifier managed and tracked by theAME 108) in the client device 104 to track media impressions on theclient device 104. For example, the AME 108 may provide instructions inthe data collector 1112 to set an AME-provided identifier in memoryspace accessible by and/or allocated to the app program 1116. The datacollector 1112 uses the identifier as a device/user identifier 114. Insuch examples, the AME-provided identifier set by the data collector1112 persists in the memory space even when the app program 1116 and thedata collector 1112 are not running. In this manner, the sameAME-provided identifier can remain associated with the client device 104for extended durations and from app to app. In some examples in whichthe data collector 1112 sets an identifier in the client device 104, theAME 108 may recruit a user of the client device 104 as a panelist, andmay store user information collected from the user during a panelistregistration process and/or collected by monitoring useractivities/behavior via the client device 104 and/or any other deviceused by the user and monitored by the AME 108. In this manner, the AME108 can associate user information of the user (from panelist datastored by the AME 108) with media impressions attributed to the user onthe client device 104.

In the illustrated example, the data collector 1112 sends the media ID1122 and the one or more device/user identifier(s) 114 as collected data1126 to the app publisher 1110. Alternatively, the data collector 1112may be configured to send the collected data 1126 to another collectionentity (other than the app publisher 1110) that has been contracted bythe AME 108 or is partnered with the AME 108 to collect media ID's(e.g., the media ID 1122) and device/user identifiers (e.g., thedevice/user identifier(s) 114) from mobile devices (e.g., the clientdevice 104). In the illustrated example, the app publisher 1110 (or acollection entity) sends the media ID 1122 and the device/useridentifier(s) 114 as impression data 102 to a server 1132 at the AME108. The impression data 102 of the illustrated example may include onemedia ID 1122 and one or more device/user identifier(s) 114 to report asingle impression of the media 1118, or it may include numerous mediaID's 1122 and device/user identifier(s) 114 based on numerous instancesof collected data (e.g., the collected data 1126) received from theclient device 104 and/or other mobile devices to report multipleimpressions of media.

In the illustrated example, the server 1132 stores the impression data102 in an AME media impressions store 1134 (e.g., a database or otherdata structure). Subsequently, the AME 108 sends the device/useridentifier(s) 114 to corresponding partner database proprietors (e.g.,the partner database proprietors 110 a, 110 b) to receive userinformation (e.g., the user information 1102 a, 1102 b) corresponding tothe device/user identifier(s) 114 from the partner database proprietors110 a, 110 b so that the AME 108 can associate the user information withcorresponding media impressions of media (e.g., the media 1118)presented at mobile devices (e.g., the client device 104).

In some examples, to protect the privacy of the user of the clientdevice 104, the media identifier 1122 and/or the device/useridentifier(s) 114 are encrypted before they are sent to the AME 108and/or to the partner database proprietors 110 a, 110 b. In otherexamples, the media identifier 1122 and/or the device/user identifier(s)114 are not encrypted.

After the AME 108 receives the device/user identifier(s) 114, the AME108 sends device/user identifier logs 1136 a, 1136 b to correspondingpartner database proprietors (e.g., the partner database proprietors 110a, 110 b). In some examples, each of the device/user identifier logs1136 a, 1136 b includes a single device/user identifier. In someexamples, some or all of the device/user identifier logs 136 a, 1136 binclude numerous aggregate device/user identifiers 114 received at theAME 108 over time from one or more mobile devices. After receiving thedevice/user identifier logs 1136 a, 1136 b, each of the partner databaseproprietors 110 a, 110 b looks up its users corresponding to thedevice/user identifiers 114 in the respective logs 136 a-b. In thismanner, each of the partner database proprietors 104 a-b collects userinformation 1102 a, 1102 b corresponding to users identified in thedevice/user identifier logs 1136 a, 1136 b for sending to the AME 108.For example, if the partner database proprietor 110 a is a wirelessservice provider and the device/user identifier log 1136 a includes IMEInumbers recognizable by the wireless service provider, the wirelessservice provider accesses its subscriber records to find users havingIMEI numbers matching the IMEI numbers received in the device/useridentifier log 1136 a. When the users are identified, the wirelessservice provider copies the users' user information to the userinformation 1102 a for delivery to the AME 108.

In some other examples, the example data collector 1112 sends thedevice/user identifier(s) 114 from the client device 104 to the apppublisher 1110 in the collected data 1126, and it also sends thedevice/user identifier(s) 114 to the media publisher 1120. In such otherexamples, the data collector 1112 does not collect the media ID 1122from the media 1118 at the client device 104 as the data collector 1112does in the example system 1100 of FIG. 11. Instead, the media publisher1120 that publishes the media 1118 to the client device 104 retrievesthe media ID 1122 from the media 1118 that it publishes. The mediapublisher 1120 then associates the media ID 1122 to the device/useridentifier(s) 114 received from the data collector 1112 executing in theclient device 104, and sends collected data 138 to the app publisher1110 that includes the media ID 1122 and the associated device/useridentifier(s) 114 of the client device 104. For example, when the mediapublisher 1120 sends the media 1118 to the client device 104, it does soby identifying the client device 104 as a destination device for themedia 1118 using one or more of the device/user identifier(s) 114received from the client device 104. In this manner, the media publisher1120 can associate the media ID 1122 of the media 1118 with thedevice/user identifier(s) 114 of the client device 104 indicating thatthe media 1118 was sent to the particular client device 104 forpresentation (e.g., to generate an impression of the media 1118).

Alternatively, in some other examples in which the data collector 1112is configured to send the device/user identifier(s) 114 to the mediapublisher 1120, and the data collector 1112 does not collect the mediaID 1122 from the media 1118 at the client device 104, the mediapublisher 1102 sends impression data 102 to the AME 108. For example,the media publisher 1120 that publishes the media 1118 to the clientdevice 104 also retrieves the media ID 1122 from the media 1118 that itpublishes, and associates the media ID 1122 with the device/useridentifier(s) 114 of the client device 104. The media publisher 1120then sends the media impression data 102, including the media ID 1122and the device/user identifier(s) 114, to the AME 108. For example, whenthe media publisher 1120 sends the media 1118 to the client device 104,it does so by identifying the client device 104 as a destination devicefor the media 1118 using one or more of the device/user identifier(s)114. In this manner, the media publisher 1120 can associate the media ID1122 of the media 1118 with the device/user identifier(s) 114 of theclient device 104 indicating that the media 1118 was sent to theparticular client device 104 for presentation (e.g., to generate animpression of the media 1118). In the illustrated example, after the AME108 receives the impression data 102 from the media publisher 1120, theAME 108 can then send the device/user identifier logs 1136 a, 1136 b tothe partner database proprietors 110 a, 110 b to request the userinformation 1102 a, 1102 b as described above.

Although the media publisher 1120 is shown separate from the apppublisher 1110 in FIG. 11, the app publisher 1110 may implement at leastsome of the operations of the media publisher 1120 to send the media1118 to the client device 104 for presentation. For example,advertisement providers, media providers, or other information providersmay send media (e.g., the media 1118) to the app publisher 1110 forpublishing to the client device 104 via, for example, the app program1116 when it is executing on the client device 104. In such examples,the app publisher 1110 implements the operations described above asbeing performed by the media publisher 1120.

Additionally or alternatively, in contrast with the examples describedabove in which the client device 104 sends identifiers to the AME 108(e.g., via the application publisher 1110, the media publisher 1120,and/or another entity), in other examples the client device 104 (e.g.,the data collector 1112 installed on the client device 104) sends theidentifiers (e.g., the user/device identifier(s) 114) directly to therespective database proprietors 110 a, 110 b (e.g., not via the AME108). In such examples, the example client device 104 sends the mediaidentifier 1122 to the AME 108 (e.g., directly or through anintermediary such as via the application publisher 1110), but does notsend the media identifier 1122 to the database proprietors 110 a, 110 b.

As mentioned above, the example partner database proprietors 110 a. 110b provide the user information 1102 a, 1102 b to the example AME 108 formatching with the media identifier 1122 to form media impressioninformation. As also mentioned above, the database proprietors 110 a,110 b are not provided copies of the media identifier 1122. Instead, theclient device 104 provides the database proprietors 110 a, 110 b withimpression identifiers 1140. An impression identifier 1140 uniquelyidentifies an impression event relative to other impression events ofthe client device 104 so that an occurrence of an impression at theclient device 104 can be distinguished from other occurrences ofimpressions. However, the impression identifier 1140 does not itselfidentify the media associated with that impression event. In suchexamples, the impression data 102 from the client device 104 to the AME108 also includes the impression identifier 1140 and the correspondingmedia identifier 1122. To match the user information 1102 a, 1102 b withthe media identifier 1122, the example partner database proprietors 110a, 110 b provide the user information 1102 a, 1102 b to the AME 108 inassociation with the impression identifier 1140 for the impression eventthat triggered the collection of the user information 1102 a, 1102 b. Inthis manner, the AME 108 can match the impression identifier 1140received from the client device 104 via the impression data 102 to acorresponding impression identifier 1140 received from the partnerdatabase proprietors 110 a, 110 b via the user information 1102 a, 1102b to associate the media identifier 1122 received from the client device104 with demographic information in the user information 1102 a, 1102 breceived from the database proprietors 110 a, 110 b.

The impression identifier 1140 of the illustrated example is structuredto reduce or avoid duplication of audience member counts for audiencesize measures. For example, the example partner database proprietors 110a, 110 b provide the user information 1102 a. 1102 b and the impressionidentifier 1140 to the AME 108 on a per-impression basis (e.g., eachtime a client device 104 sends a request including an encryptedidentifier and an impression identifier 1140 to the partner databaseproprietor 110 a, 110 b) and/or on an aggregated basis. When aggregateimpression data is provided in the user information 1102 a, 1102 b, theuser information 1102 a, 1102 b includes indications of multipleimpressions (e.g., multiple impression identifiers 1140) at mobiledevices. In some examples, aggregate impression data includes uniqueaudience values (e.g., a measure of the quantity of unique audiencemembers exposed to particular media), total impression count, frequencyof impressions, etc. In some examples, the individual logged impressionsare not discernable from the aggregate impression data.

As such, it is not readily discernable from the user information 1102 a,1102 b whether instances of individual user-level impressions logged atthe database proprietors 110 a, 110 b correspond to the same audiencemember such that unique audience sizes indicated in the aggregateimpression data of the user-information 1102 a, 1102 b are inaccuratefor being based on duplicate counting of audience members. However, theimpression identifier 1140 provided to the AME 108 enables the AME 108to distinguish unique impressions and avoid overcounting a number ofunique users and/or devices accessing the media. For example, therelationship between the user information 1102 a from the partner Adatabase proprietor 110 a and the user information 1102 b from thepartner B database proprietor 110 b for the client device 104 is notreadily apparent to the AME 108. By including an impression identifier1140 (or any similar identifier), the example AME 108 can associate userinformation corresponding to the same user between the user information1102 a, 1102 b based on matching impression identifiers 140 stored inboth of the user information 1102 a, 1102 b. The example AME 108 can usesuch matching impression identifiers 1140 across the user information1102 a, 1102 b to avoid overcounting mobile devices and/or users (e.g.,by only counting unique users instead of counting the same user multipletimes).

A same user may be counted multiple times if, for example, an impressioncauses the client device 104 to send multiple user/device identifiers tomultiple different database proprietors 110 a, 110 b without animpression identifier (e.g., the impression identifier 1140). Forexample, a first one of the database proprietors 110 a sends first userinformation 1102 a to the AME 108, which signals that an impressionoccurred. In addition, a second one of the database proprietors 110 bsends second user information 1102 b to the AME 108, which signals(separately) that an impression occurred. In addition, separately, theclient device 104 sends an indication of an impression to the AME 108.Without knowing that the user information 1102 a, 1102 b is from thesame impression, the AME 108 has an indication from the client device104 of a single impression and indications from the database proprietors110 a, 110 b of multiple impressions.

To avoid overcounting impressions, the AME 108 can use the impressionidentifier 1140. For example, after looking up user information 1102 a,1102 b, the example partner database proprietors 110 a, 110 b transmitthe impression identifier 1140 to the AME 108 with corresponding userinformation 1102 a, 1102 b. The AME 108 matches the impressionidentifier 1140 obtained directly from the client device 104 to theimpression identifier 1140 received from the database proprietors 110 a,110 b with the user information 102 a-b to thereby associate the userinformation 1102 a, 1102 b with the media identifier 1122 and togenerate impression information. This is possible because the AME 108received the media identifier 1122 in association with the impressionidentifier 1140 directly from the client device 104. Therefore, the AME108 can map user data from two or more database proprietors 110 a, 110 bto the same media exposure event, thus avoiding double counting.

Each unique impression identifier 1140 in the illustrated example isassociated with a specific impression of media on the client device 104.The partner database proprietors 110 a, 110 b receive the respectiveuser/device identifiers 114 and generate the user information 1102 a,1102 b independently (e.g., without regard to others of the partnerdatabase proprietors 104 a-b) and without knowledge of the mediaidentifier 1122 involved in the impression. Without an indication that aparticular user demographic profile in the user information 1102 a(received from the partner database proprietor 110 a) is associated with(e.g., the result of) the same impression at the client device 104 as aparticular user demographic profile in the user information 1102 b(received from the partner database proprietor 110 b independently ofthe user information 1102 a received from the partner databaseproprietor 110 a), and without reference to the impression identifier1140, the AME 108 may not be able to associate the user information 1102a with the user information 1102 b and/or cannot determine that thedifferent pieces of user information 1102 a, 1102 b are associated witha same impression and could, therefore, count the user information 1102a and the user information 1102 b as corresponding to two differentusers/devices and/or two different impressions.

The examples of FIG. 11 illustrate methods and apparatus for collectingimpression data at an audience measurement entity (or other entity). Theexamples of FIG. 11 may be used to collect impression information forany type of media, including static media (e.g., advertising images),streaming media (e.g., streaming video and/or audio, including content,advertising, and/or other types of media), and/or other types of media.For static media (e.g., media that does not have a time component suchas images, text, a webpage, etc.), the example AME 108 records animpression once for each occurrence of the media being presented,delivered, or otherwise provided to the client device 104. For streamingmedia (e.g., video, audio, etc.), the example AME 108 measuresdemographics for media occurring over a period of time. For example, theAME 108 (e.g., via the app publisher 1110 and/or the media publisher1120) provides beacon instructions to a client application or clientsoftware (e.g., the OS 1114, the web browser 1117, the app 1116, etc.)executing on the client device 104 when media is loaded at clientapplication/software 1114-1117. In some examples, the beaconinstructions are embedded in the streaming media and delivered to theclient device 106 via the streaming media. In some examples, the beaconinstructions cause the client application/software 1114-1117 to transmita request (e.g., a pingback message) to an impression monitoring server1132 at regular and/or irregular intervals (e.g., every minute, every 30seconds, every 2 minutes, etc.). The example impression monitoringserver 1132 identifies the requests from the web browser 1117 and, incombination with one or more database proprietors, associates theimpression information for the media with demographics of the user ofthe web browser 1117.

In some examples, a user loads (e.g., via the browser 1117) a web pagefrom a web site publisher, in which the web page corresponds to aparticular 60-minute video. As a part of or in addition to the exampleweb page, the web site publisher causes the data collector 1112 to senda pingback message (e.g., a beacon request) to a beacon server 1142 by,for example, providing the browser 1117 with beacon instructions. Forexample, when the beacon instructions are executed by the examplebrowser 1117, the beacon instructions cause the data collector 1112 tosend pingback messages (e.g., beacon requests, HTTP requests, pings) tothe impression monitoring server 1132 at designated intervals (e.g.,once every minute or any other suitable interval). The example beaconinstructions (or a redirect message from, for example, the impressionmonitoring server 1132 or a database proprietor 104 a-b) further causethe data collector 1112 to send pingback messages or beacon requests toone or more database proprietors 110 a, 110 b that collect and/ormaintain demographic information about users. The database proprietor110 a, 110 b transmits demographic information about the user associatedwith the data collector 1112 for combining or associating with theimpression determined by the impression monitoring server 1132. If theuser closes the web page containing the video before the end of thevideo, the beacon instructions are stopped, and the data collector 1112stops sending the pingback messages to the impression monitoring server1132. In some examples, the pingback messages include timestamps and/orother information indicative of the locations in the video to which thenumerous pingback messages correspond. By determining a number and/orcontent of the pingback messages received at the impression monitoringserver 1132 from the client device 104, the example impressionmonitoring server 1132 can determine that the user watched a particularlength of the video (e.g., a portion of the video for which pingbackmessages were received at the impression monitoring server 1132).

The client device 104 of the illustrated example executes a clientapplication/software 1114-1117 that is directed to a host website (e.g.,www.acme.com) from which the media 1118 (e.g., audio, video, interactivemedia, streaming media, etc.) is obtained for presenting via the clientdevice 104. In the illustrated example, the media 1118 (e.g.,advertisements and/or content) is tagged with identifier information(e.g., a media ID 1122, a creative type ID, a placement ID, a publishersource URL, etc.) and a beacon instruction. The example beaconinstruction causes the client application/software 1114-1117 to requestfurther beacon instructions from a beacon server 1142 that will instructthe client application/software 1114-1117 on how and where to sendbeacon requests to report impressions of the media 1118. For example,the example client application/software 1114-1117 transmits a requestincluding an identification of the media 1118 (e.g., the mediaidentifier 1122) to the beacon server 1142. The beacon server 1142 thengenerates and returns beacon instructions 1144 to the example clientdevice 104. Although the beacon server 1142 and the impressionmonitoring server 132 are shown separately, in some examples the beaconserver 1142 and the impression monitoring server 1132 are combined. Inthe illustrated example, beacon instructions 1144 include URLs of one ormore database proprietors (e.g., one or more of the partner databaseproprietors 110 a, 110 b) or any other server to which the client device104 should send beacon requests (e.g., impression requests). In someexamples, a pingback message or beacon request may be implemented as anHTTP request. However, whereas a transmitted HTTP request identifies awebpage or other resource to be downloaded, the pingback message orbeacon request includes the audience measurement information (e.g., adcampaign identification, content identifier, and/or device/useridentification information) as its payload. The server to which thepingback message or beacon request is directed is programmed to log theaudience measurement data of the pingback message or beacon request asan impression (e.g., an ad and/or content impression depending on thenature of the media tagged with the beaconing instructions). In someexamples, the beacon instructions received with the tagged media 1118include the beacon instructions 1144. In such examples, the clientapplication/software 1114-1117 does not need to request beaconinstructions 1144 from a beacon server 1142 because the beaconinstructions 1144 are already provided in the tagged media 1118.

When the beacon instructions 1144 are executed by the client device 104,the beacon instructions 1144 cause the client device 104 to send beaconrequests (e.g., repeatedly at designated intervals) to a remote server(e.g., the impression monitoring server 1132, the media publisher 1120,the database proprietors 110 a, 110 b, or another server) specified inthe beacon instructions 1144. In the illustrated example, the specifiedserver is a server of the AME 108, namely, at the impression monitoringserver 1132. The beacon instructions 1144 may be implemented usingJavaScript or any other types of instructions or script executable via aclient application (e.g., a web browser) including, for example, Java,HTML, etc.

FIG. 12 depicts an example age prediction model 306 generated based on aclassification tree (e.g., a decision tree) that uses subscriberactivity metrics 138 to predict real ages (e.g., predicted age PDFs 305)of subscribers of a database proprietor 110 (FIG. 1). The examplesubscriber activity metrics 138 include, but are not limited to, userage reported to database proprietor, number of contacts (e.g., friends,connections, etc.), median age of contacts, privacy of birthdate (e.g.,birthdate is or is not displayed to contacts), year of high schoolgraduation, gender, whether a current address is associated with thesubscriber's profile, whether a profile picture is associated with thesubscriber's profile, whether a mobile phone number is associated withthe subscriber's profile, subscriber activity that occurred within thelast thirty days, subscriber activity that occurred within the lastseven days, email address the subscriber used to register is in the .edudomain, percent of contacts that are female, degree of privacy settings(e.g., high privacy, medium privacy, low privacy, etc.), frequency oflogin (F_(LOGIN)), frequency of posting status updates (F_(POST)),percentage of logins done via a mobile device (LOGIN), whether arelationship status is associated with the subscriber's profile, numberof days since the subscriber registered with the database proprietor,number of messages sent in the past seven days, webpages viewed in thepast seven days, etc.

In the illustrated example of FIG, 12, the age prediction model 306 is aclassification tree. In the illustrated example of FIG. 12, theclassification tree implementing the age prediction model 306 mayinclude many nodes and terminals. A detailed view of a portion of theexample age prediction model 306 is shown in FIG. 12 in which theexample age prediction model has intermediate (e.g., decision) nodes1200 a-1200 c and terminal (e.g., prediction) nodes 1202 a-1202 d. Theintermediate nodes 1200 a-1200 c represent test conditions based on thesubscriber activity metrics 138 that result in branching paths. Forexample, an intermediate node 1200 a may represent a test conditionbased on the frequency with which a subscriber logs into the databaseproprietor 110. In such an example, the test condition may be whetherthe subscriber logs into the database proprietor either (i) less thanfive times per week (<5 times/week), or (ii) greater than or equal tofive times per week (>5 times/week). The branching paths may either leadto another intermediate node 1200 b-1200 c or one of the terminal nodes1202 a-1202 d. In the illustrated example, the example terminal nodes1202 a-1202 d represent a classification (e.g., the predicted age PDF305) based on the subscriber activity metrics 138 that is output fromthe age prediction model 306 (e.g., and used by the age predictor 304 ofFIG. 3).

However, the predictive value of the age prediction model 306 modeldegrades over time as subscriber behaviors (e.g., as measured bysubscriber activity metrics 138) on which the age prediction model 306is based change over time. In some examples, small changes insubscriber-behavior can change the terminal node 1202 a-1202 d reachedby the age prediction model 306. The manner in which outcomes are affectby such small behavior changes is observable using the example ageprediction model 306 of FIG. 12. For example, in FIG. 12, the ageprediction model 306 is used to predict the real age (e.g., a predictedage PDF 305 associated with terminal nodes 1202 a-1202 d) of asubscriber (e.g. “Subscriber A”) at day D=0 and at day D=822. In theillustrated example, the age prediction model 306 is generated on dayD=0. On day D=0, the login frequency of the subscriber A is greater thanor equal to five times per week. As a result, a decision at anintermediate node 1200 a using login frequency (F_(LOGIN)) as a testcondition, the branches down a path that leads to an intermediate node1202 c. In the illustrated example, a decision at an intermediate node1200 c using login type (LOGIN) as a test condition branches down a pathleading to a terminal node 1202 c because less than 50% of the logins bysubscriber A are via a mobile device type when logging into the databaseproprietor 110. In the illustrated example, by day D=822 (e.g., 822 daysafter day D=0), the subscriber activity metrics 138 of the subscriber Achange so that frequency of login (F_(LOGIN)) is less than five timesper week. As a result, if the prediction model 306 generated on day D=0is used to predict the real age of the subscriber on day D=822, theresulting terminal node 1202 b would be different compared to theresults on day D=0.

FIG. 13 is a flow diagram representative of example machine readableinstructions 1300 that may be executed to implement the example audiencemeasurement entity 108 of FIG. 6 and/or the age corrector 124 of FIG. 3to correct for time-based deterioration of an age prediction model(e.g., the age prediction model 306 of FIG. 3). The example instructions1300 may be executed after collecting impression information (e.g., viathe AME impressions collector 618 of FIG. 6) and receiving demographicinformation (e.g., numbers of impressions and/or audience countsattributed to demographic groups, where the demographic groups aredefined at least partially based on ages) corresponding to theimpression information (e.g., via the D_(P) impressions collector 630).

The example age predictor 304 of FIG. 3 estimates first ages of theaudience members based on demographic information received from adatabase proprietor (e.g., the database proprietor 110) (block 1302). Inthe example of FIG. 13, the first ages correspond to a first time, suchas the time at which an age prediction model 306 used to estimate thefirst ages is trained or calibrated.

The example age predictor 304 estimates second ages of the audiencemembers based on the demographic information (block 1304). In theexample of FIG. 13, the second ages correspond to a second time that isafter the first time. The example age predictor 304 uses the same ageprediction model 306 used to estimate the first ages, which may havedecreased in accuracy between the first time and the second time.

The example age predictor 304 estimates a third age of an audiencemember who is not included in the audience members from the databaseproprietor 110 (block 1306). In the example of FIG. 13, the third age isestimated using the same age prediction model used to estimate the firstages and the second ages, and corresponds to the second time. As aresult, the third age may have a reduced accuracy compared to if the ageprediction model 306 was recently trained and/or calibrated. In someexamples, the age predictor 304 estimates of the third age in responseto determining that the audience member has not been previouslyidentified. For example, the age predictor 304 may determine that theaudience member not being associated with an AME identifier such as anAME cookie.

Blocks 1302, 1304, and/or 1306 may be implemented as described above inconnection with FIGS. 7 and/or 8 above. For example, the predictedfirst, second, and third ages may each have multiple ages and associatedprobabilities based on the definitions of the age prediction probabilitydensity functions and/or the decision tree. As described below, theexample model corrector 312 may correct the ages in the age predictionPDFs for deterioration of the age prediction model that is used todetermine the age prediction PDFs. In some examples, the age predictionPDFs are converted to a single age by, for example, weighting the agesin the age prediction PDFs by the respective probabilities of thoseages. In some examples, the conversion of age prediction PDFs to agevalues may be influenced by additional correction factors.

In some examples, the age predictor 304 performs blocks 1302, 1304,and/or 1306 in response to request(s) by the model corrector 312. Insome examples, the model corrector 312 selects the audience members fordetermination of the first and second ages from a larger set of audiencemembers based on the third age. In some such examples, the age predictor304 performs block 1306 prior to blocks 1302 and 1304, and the audiencemembers are selected to be within an upper threshold age difference fromthe third age. In some other examples, the audience members are selectedfrom the larger set of audience members to be within a same age bucketas the third age.

The example model corrector 312 of FIG. 3 applies a window function tothe second ages to determine a distribution of ages (block 1308). In theexample of FIG. 13, the model corrector 312 uses the third age of theaudience member as a mean of the distribution. The example windowfunction may be a probability density function value (e.g., Equation 6below), in which the third age is the mean μ and the standard deviationσ value is 1 but may be selected based on empirical observations.

$\begin{matrix}{{f( {{x\mu},\sigma} )} = {\frac{1}{\sigma \sqrt{2\pi}}^{- \frac{{({x - \mu})}^{2}}{2\sigma^{2}}}}} & ( {{Equation}\mspace{14mu} 6} )\end{matrix}$

The second ages (input as x in Equation 6) are used to calculate therespective window values. When the window values are determined, theexample model corrector 312 multiplies the window values of the secondages by respective ones of the first ages to determine corrected firstage components (block 1310). For example, a window value that iscalculated for a second age of a first subscriber is multiplied by thefirst age of the same first sub scriber.

The example model corrector 312 sums the corrected first age componentsand divides the total by a sum of the window values to determine anestimated age of the audience member at the first time (block 1312). Forexample, the model corrector 312 may determine the estimated age of theaudience member at the first time as a weighted average of the firstages, using the window values determined from the third age as theweights. As the third age changes, the weights applied to the first ageschange using the example window function (e.g., distribution).

The example model corrector 312 determines the corrected age of theaudience member at the second time based on the estimated age of thesubscriber at the first time and a time difference between the first andsecond times (block 1314). For example, the model corrector 312 ages theaudience member from the corrected age at the first time (determined inblock 1312) to the later time. In other words, if the difference betweenthe first time and the later time is 2.00 years, the example modelcorrector 312 adds 2.00 years to the corrected age determined in block1312 to obtain the corrected age at the later time.

The example instructions 1300 then end and/or repeat for anothersubscriber. Example implementations of FIG. 13 are described below inconnection with FIGS. 14 and 15.

FIG. 14 is a graph of an example distribution 1400 that may be generatedby the model corrector 312 of FIG. 3 to correct for deterioration of theage prediction model 306. The model corrector 312 uses a predicted ageof a new audience member at the later time (e.g., significantly laterthan the training or calibration of the age prediction model) as themean value 1402 of the distribution 1400. In the example of FIG. 14, themean value 1402 is 30.00 years old. Table 1 below includes firstestimated ages of a group of audience members for a first time (e.g., atime at which the age prediction model 306 is trained or calibrated,such as t=0 years), second estimated ages of the group of audiencemembers for a second time after the first time (e.g., t=2 years), anddistribution values based on the second ages and the predicted age. Inthe example of FIG. 14 and Table 1, the distribution values areprobability density function values. The graph of FIG. 14 shows thewindow values as a function of the corresponding estimated age at thelater time.

TABLE 6 Example Estimated Ages of a group of 100 Audience Members attimes t = 0 and t = 2, and Window Function values of ages at time t = 2,for a distribution mean of 30.00 Model Model Index t = 0 t = 2 window 127.00 28.09 0.065 2 25.13 26.91 0.003 3 39.28 40.60 0.000 4 37.21 38.310.000 5 22.25 23.53 0.000 6 35.55 37.38 0.000 7 39.75 40.93 0.000 839.64 41.37 0.000 9 31.50 33.21 0.002 10 31.95 33.20 0.002 11 39.7641.65 0.000 12 24.73 26.01 0.000 13 26.82 28.06 0.061 14 32.39 34.040.000 15 28.53 29.60 0.369 16 27.51 28.89 0.215 17 33.78 34.97 0.000 1835.28 36.78 0.000 19 26.46 27.62 0.023 20 39.71 41.02 0.000 21 20.5922.27 0.000 22 30.68 32.61 0.013 23 39.92 41.86 0.000 24 33.84 35.130.000 25 40.00 41.47 0.000 26 23.74 25.63 0.000 27 33.12 34.24 0.000 2832.38 33.87 0.000 29 22.22 23.86 0.000 30 29.87 31.51 0.127 31 30.3232.16 0.039 32 22.89 24.07 0.000 33 28.13 29.62 0.371 34 31.46 32.710.010 35 27.29 29.10 0.265 36 33.04 34.79 0.000 37 37.65 39.19 0.000 3828.94 30.92 0.262 39 27.57 29.17 0.283 40 21.91 23.72 0.000 41 21.1023.08 0.000 42 21.97 23.72 0.000 43 32.08 33.37 0.001 44 34.82 35.900.000 45 29.73 31.69 0.095 46 26.86 28.60 0.150 47 32.01 33.80 0.000 4833.20 35.06 0.000 49 39.48 40.85 0.000 50 34.16 35.59 0.000 51 27.1628.33 0.099 52 23.62 24.95 0.000 53 39.58 41.39 0.000 54 20.06 21.540.000 55 38.34 39.85 0.000 56 29.46 30.87 0.274 57 23.98 25.95 0.000 5835.40 36.62 0.000 59 26.51 27.76 0.033 60 34.89 36.83 0.000 61 35.8237.30 0.000 62 35.91 37.26 0.000 63 26.69 28.30 0.095 64 31.43 32.790.008 65 24.95 26.12 0.000 66 37.68 39.29 0.000 67 21.37 23.17 0.000 6829.31 30.33 0.378 69 26.39 27.61 0.023 70 28.97 30.80 0.290 71 28.2229.70 0.381 72 24.22 25.26 0.000 73 20.77 22.30 0.000 74 34.70 36.180.000 75 29.93 31.53 0.123 76 37.33 39.03 0.000 77 29.29 30.46 0.358 7821.53 22.85 0.000 79 35.23 36.47 0.000 80 23.44 25.13 0.000 81 22.2424.12 0.000 82 38.35 40.03 0.000 83 23.21 24.70 0.000 84 39.08 40.210.000 85 29.83 31.29 0.173 86 25.23 26.68 0.002 87 32.96 34.70 0.000 8832.65 33.70 0.000 89 26.71 28.21 0.080 90 30.69 31.76 0.085 91 27.9229.85 0.395 92 35.68 37.57 0.000 93 34.26 35.77 0.000 94 34.58 35.940.000 95 37.05 38.65 0.000 96 25.54 27.06 0.005 97 39.70 41.01 0.000 9827.40 28.71 0.173 99 28.33 29.66 0.376 100 35.35 37.33 0.000

The example model corrector 312 multiplies the window values of Table 6above by the corresponding ages at time t=0 of Table 6 above (e.g.,27.00*0.065 for audience member 1, 25.13*0.003 for audience member 2,etc.) to apply the distribution to the ages at the time t=0 (e.g., agecomponents at time t=0). The model corrector 312 then sums the agecomponents to determine a corrected age of the new audience member attime t=0. In the example of Table 6, there are 100 age components to beadded. Using the example ages in Table 6 and an estimated age of 30.00at time t=2, the example model corrector 312 determines the correctedage at time t=0 to be 28.42 years. To determine the corrected age of thenew audience member at time t=2, the example model corrector 312 addsthe difference between times t=0 and t=2 (e.g., 2 years) to thecorrected age at time t=0. In the example of FIG. 14 and Table 6, theexample model corrector 312 determines the corrected age at time t=2 tobe 30.42 years (e.g., 28.42 years+(2−0) years).

FIG. 15 is a graph of another example distribution 1500 that may begenerated by the model corrector 312 of FIG. 3 to correct fordeterioration of the age prediction model 306. The example distribution1500 of FIG. 15 uses a predicted age of 35.00 as the mean 1502 insteadof the mean of 30.00 of FIG. 14. Table 7 below includes distributionvalues for the second estimated ages using the mean value 1502 of 35.00.

TABLE 7 Example Estimated Ages of a group of 100 Audience Members attimes t = 0 and t = 2, and Window Function values of ages at time t = 2,for a distribution mean of 35.00 Model Model Index t = 0 t = 2 Window 127.00 28.09 0.000 2 25.13 26.91 0.000 3 39.28 40.60 0.000 4 37.21 38.310.002 5 22.25 23.53 0.000 6 35.55 37.38 0.024 7 39.75 40.93 0.000 839.64 41.37 0.000 9 31.50 33.21 0.081 10 31.95 33.20 0.079 11 39.7641.65 0.000 12 24.73 26.01 0.000 13 26.82 28.06 0.000 14 32.39 34.040.251 15 28.53 29.60 0.000 16 27.51 28.89 0.000 17 33.78 34.97 0.399 1835.28 36.78 0.082 19 26.46 27.62 0.000 20 39.71 41.02 0.000 21 20.5922.27 0.000 22 30.68 32.61 0.023 23 39.92 41.86 0.000 24 33.84 35.130.396 25 40.00 41.47 0.000 26 23.74 25.63 0.000 27 33.12 34.24 0.299 2832.38 33.87 0.212 29 22.22 23.86 0.000 30 29.87 31.51 0.001 31 30.3232.16 0.007 32 22.89 24.07 0.000 33 28.13 29.62 0.000 34 31.46 32.710.029 35 27.29 29.10 0.000 36 33.04 34.79 0.390 37 37.65 39.19 0.000 3828.94 30.92 0.000 39 27.57 29.17 0.000 40 21.91 23.72 0.000 41 21.1023.08 0.000 42 21.97 23.72 0.000 43 32.08 33.37 0.105 44 34.82 35.900.266 45 29.73 31.69 0.002 46 26.86 28.60 0.000 47 32.01 33.80 0.194 4833.20 35.06 0.398 49 39.48 40.85 0.000 50 34.16 35.59 0.334 51 27.1628.33 0.000 52 23.62 24.95 0.000 53 39.58 41.39 0.000 54 20.06 21.540.000 55 38.34 39.85 0.000 56 29.46 30.87 0.000 57 23.98 25.95 0.000 5835.40 36.62 0.108 59 26.51 27.76 0.000 60 34.89 36.83 0.075 61 35.8237.30 0.029 62 35.91 37.26 0.031 63 26.69 28.30 0.000 64 31.43 32.790.035 65 24.95 26.12 0.000 66 37.68 39.29 0.000 67 21.37 23.17 0.000 6829.31 30.33 0.000 69 26.39 27.61 0.000 70 28.97 30.80 0.000 71 28.2229.70 0.000 72 24.22 25.26 0.000 73 20.77 22.30 0.000 74 34.70 36.180.200 75 29.93 31.53 0.001 76 37.33 39.03 0.000 77 29.29 30.46 0.000 7821.53 22.85 0.000 79 35.23 36.47 0.135 80 23.44 25.13 0.000 81 22.2424.12 0.000 82 38.35 40.03 0.000 83 23.21 24.70 0.000 84 39.08 40.210.000 85 29.83 31.29 0.000 86 25.23 26.68 0.000 87 32.96 34.70 0.381 8832.65 33.70 0.172 89 26.71 28.21 0.000 90 30.69 31.76 0.002 91 27.9229.85 0.000 92 35.68 37.57 0.015 93 34.26 35.77 0.297 94 34.58 35.940.257 95 37.05 38.65 0.001 96 25.54 27.06 0.000 97 39.70 41.01 0.000 9827.40 28.71 0.000 99 28.33 29.66 0.000 100 35.35 37.33 0.026

Using the example ages in Table 7 and an estimated age of 35.00 at timet=2, the example model corrector 312 determines the corrected age attime t=0 to be 33.55 years. To determine the corrected age of the newaudience member at time t=2, the example model corrector 312 adds thedifference between times t=0 and t=2 (e.g., 2 years) to the correctedage at time t=0. In the example of FIG. 15 and Table 2, the examplemodel corrector 312 determines the corrected age at time t=2 to be 35.55years (e.g., 33.55 years+(2−0) years). While example times t=0 and t=2are used in the above examples, any other times may be used.

From the foregoing, it will be appreciated that examples have beendisclosed which allow association of accurate age-based demographicgroups with impressions generated to exposure to media. Additionally, itwill be appreciated that examples have been disclosed which enhance theoperations of a computer to improve the accuracy of impression-baseddata so that computers and processing systems therein can be relied uponto produce audience analysis information with higher accuracies.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. A method to correct for deterioration of ademographic model to associate demographic information with mediaimpression information, comprising: collecting, at a processor at anaudience measurement entity, messages indicating first impressions of amedia item delivered to devices via the Internet, the messagesidentifying the media item presented at the devices; receiving, at theprocessor at the audience measurement entity, first demographicinformation describing first numbers of impressions of the media itemand first numbers of audience members attributed to respectivedemographic groups by a database proprietor, the first numbers of theimpressions and the first numbers of audience members corresponding tothe first impressions of the media; estimating first ages of theaudience members based on the first demographic information, the firstages corresponding to a first time; estimating second ages of theaudience members based on the first demographic information, the secondages corresponding to a second time after the first time; estimating athird age of an audience member who is not included in the audiencemembers from the database proprietor, the third age corresponding to thesecond time; estimating a corrected age of the audience member at thesecond time by: applying a window function to the second ages todetermine a distribution of ages based on the third age of the audiencemember as a mean of the distribution; multiplying window values of thesecond ages by respective ones of the first ages to determine correctedfirst age components; summing the corrected first age components anddividing a total of the corrected first age components by a sum of thewindow values to determine an estimated age of the audience member atthe first time; and determining the corrected age of the audience memberat the second time based on the estimated age of the audience member atthe first time and a time difference between the first and second times;and determining ratings information for the media by attributingimpressions and audience counts to the media using the corrected age ofthe audience member instead of the third age.
 2. The method as definedin claim 1, wherein the window function includes a probability densityfunction based on a Gaussian distribution.
 3. The method as defined inclaim 1, wherein the estimating of the first ages includes determining apredicted age probability density function.
 4. The method as defined inclaim 3, wherein the estimating of the second ages includes applying anaging factor to an age bucket in the predicted age probability densityfunction.
 5. The method as defined in claim 1, further includingselecting the audience members from a larger set of audience membersbased on the second ages being within an age range, the age range beingbased on the third age.
 6. The method as defined in claim 1, furtherincluding transmitting audience measurement entity identifiers to thedevices in response to at least a portion of the messages, theestimating of the third age being in response to determining, based onthe audience member not being associated with an audience measuremententity identifier, that the audience member has not been previouslyidentified.
 7. The method as defined in claim 6, further includingsending re-direct messages in response to at least a portion of themessages, the re-direct messages to cause at least a portion of thedevices to send third messages to the database proprietor, the firstdemographic information being received based on the third messages. 8.An apparatus to correct for deterioration of a demographic model toassociate demographic information with media impression information,comprising: a first impressions collector to collect messages indicatingfirst impressions of a media item delivered to devices via the Internet,the messages identifying the media item presented at the devices; asecond impressions collector to receive first demographic informationdescribing first numbers of impressions of the media item and firstnumbers of audience members attributed to respective demographic groupsby a database proprietor, the first numbers of the impressions and thefirst numbers of audience members corresponding to the first impressionsof the media; an age predictor to: estimate first ages of the audiencemembers based on the first demographic information, the first agescorresponding to a first time; estimate second ages of the audiencemembers based on the first demographic information, the second agescorresponding to a second time after the first time; estimate a thirdage of an audience member who is not included in the audience membersfrom the database proprietor, the third age corresponding to the secondtime; a model corrector to: apply a window function to the second agesto determine a distribution of ages based on the third age of theaudience member as a mean of the distribution; multiply window values ofthe second ages by respective ones of the first ages to determinecorrected first age components; divide a sum of the corrected first agecomponents by a sum of the window values to determine an estimated ageof the audience member at the first time; and determine the correctedage of the audience member at the second time based on the estimated ageof the audience member at the first time and a time difference betweenthe first and second times; and a ratings determiner to determineratings information for the media by attributing impressions andaudience counts to the media using the corrected age of the audiencemember instead of the third age.
 9. The apparatus as defined in claim 8,wherein the model corrector is to apply the window function by applyinga probability density function based on a Gaussian distribution.
 10. Theapparatus as defined in claim 8, wherein the age predictor is toestimate the first ages by determining a predicted age probabilitydensity function.
 11. The apparatus as defined in claim 10, wherein theage predictor is to estimate the second ages by applying an aging factorto an age bucket in the predicted age probability density function. 12.The apparatus as defined in claim 8, wherein the model corrector is toselect the audience members from a larger set of audience members basedon the second ages being within an age range, the age range being basedon the third age.
 13. The apparatus as defined in claim 8, wherein thefirst impressions collector is to transmit audience measurement entityidentifiers to the devices in response to at least a portion of themessages, the age predictor to estimate of the third age in response todetermining, based on the audience member not being associated with anaudience measurement entity identifier, that the audience member has notbeen previously identified.
 14. The apparatus as defined in claim 8,wherein the first impressions collector is to send re-direct messages inresponse to at least a portion of the messages, the re-direct messagesto cause at least a portion of the devices to send third messages to thedatabase proprietor, the first demographic information being receivedbased on the third messages.
 15. A tangible computer readable storagemedium comprising computer readable instructions which, when executed,cause a processor to at least: collect messages indicating firstimpressions of a media item delivered to devices via the Internet, themessages identifying the media item presented at the devices; accessfirst demographic information describing first numbers of impressions ofthe media item and first numbers of audience members attributed torespective demographic groups by a database proprietor, the firstnumbers of the impressions and the first numbers of audience memberscorresponding to the first impressions of the media; estimate first agesof the audience members based on the first demographic information, thefirst ages corresponding to a first time; estimate second ages of theaudience members based on the first demographic information, the secondages corresponding to a second time after the first time; estimate athird age of an audience member who is not included in the audiencemembers from the database proprietor, the third age corresponding to thesecond time; estimate a corrected age of the audience member at thesecond time by: applying a window function to the second ages todetermine a distribution of ages based on the third age of the audiencemember as a mean of the distribution; multiply window values of thesecond ages by respective ones of the first ages to determine correctedfirst age components; summing the corrected first age components anddividing a total of the corrected first age components by a sum of thewindow values to determine an estimated age of the audience member atthe first time; and determining the corrected age of the audience memberat the second time based on the estimated age of the audience member atthe first time and a time difference between the first and second times;and determine ratings information for the media by attributingimpressions and audience counts to the media using the corrected age ofthe audience member instead of the third age.
 16. The tangible computerreadable storage medium as defined in claim 15, wherein the windowfunction includes a probability density function based on a Gaussiandistribution.
 17. The tangible computer readable storage medium asdefined in claim 15, wherein the instructions are to cause the processorto estimate the first ages by determining a predicted age probabilitydensity function.
 18. The tangible computer readable storage medium asdefined in claim 17, wherein the instructions are to cause the processorto estimate the second ages by applying an aging factor to an age bucketin the predicted age probability density function.
 19. The tangiblecomputer readable storage medium as defined in claim 15, wherein theinstructions are further to cause the processor to select the audiencemembers from a larger set of audience members based on the second agesbeing within an age range, the age range being based on the third age.20. The tangible computer readable storage medium as defined in claim15, wherein the instructions are further to cause the processor totransmit audience measurement entity identifiers to the devices inresponse to at least a portion of the messages, the instructions tocause the processor to estimate the third age in response todetermining, based on the audience member not being associated with anaudience measurement entity identifier, that the audience member has notbeen previously identified.